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Yang T, Hu H, Li X, Meng Q, Lu H, Huang Q. An efficient Fusion-Purification Network for Cervical pap-smear image classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 251:108199. [PMID: 38728830 DOI: 10.1016/j.cmpb.2024.108199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 02/28/2024] [Accepted: 04/21/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND AND OBJECTIVES In cervical cell diagnostics, autonomous screening technology constitutes the foundation of automated diagnostic systems. Currently, numerous deep learning-based classification techniques have been successfully implemented in the analysis of cervical cell images, yielding favorable outcomes. Nevertheless, efficient discrimination of cervical cells continues to be challenging due to large intra-class and small inter-class variations. The key to dealing with this problem is to capture localized informative differences from cervical cell images and to represent discriminative features efficiently. Existing methods neglect the importance of global morphological information, resulting in inadequate feature representation capability. METHODS To address this limitation, we propose a novel cervical cell classification model that focuses on purified fusion information. Specifically, we first integrate the detailed texture information and morphological structure features, named cervical pathology information fusion. Second, in order to enhance the discrimination of cervical cell features and address the data redundancy and bias inherent after fusion, we design a cervical purification bottleneck module. This model strikes a balance between leveraging purified features and facilitating high-efficiency discrimination. Furthermore, we intend to unveil a more intricate cervical cell dataset: Cervical Cytopathology Image Dataset (CCID). RESULTS Extensive experiments on two real-world datasets show that our proposed model outperforms state-of-the-art cervical cell classification models. CONCLUSIONS The results show that our method can well help pathologists to accurately evaluate cervical smears.
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Affiliation(s)
- Tianjin Yang
- College of Computer and Information, Hohai University, Nanjing, 211100, PR China.
| | - Hexuan Hu
- College of Computer and Information, Hohai University, Nanjing, 211100, PR China.
| | - Xing Li
- College of information Science and Technology & College of Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, PR China.
| | - Qing Meng
- College of Computer and Information, Hohai University, Nanjing, 211100, PR China.
| | - Hao Lu
- College of Computer and Information, Hohai University, Nanjing, 211100, PR China.
| | - Qian Huang
- College of Computer and Information, Hohai University, Nanjing, 211100, PR China.
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2
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Wasswa W. Automated innovation and impact. Science 2024; 384:42. [PMID: 38574148 DOI: 10.1126/science.ado4541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
Online platforms promote community change, from challenge to commercialization.
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Affiliation(s)
- William Wasswa
- Global Auto Systems LTD Uganda, Mbarara, Uganda
- Department of Biomedical Sciences and Engineering, Mbarara University of Science and Technology, Mbarara, Uganda
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3
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Lapsina S, Riond B, Hofmann-Lehmann R, Stirn M. Comparison of Sysmex XN-V body fluid mode and deep-learning-based quantification with manual techniques for total nucleated cell count and differential count for equine bronchoalveolar lavage samples. BMC Vet Res 2024; 20:48. [PMID: 38317167 PMCID: PMC10840287 DOI: 10.1186/s12917-024-03884-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 01/17/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND Bronchoalveolar lavage (BAL) is a diagnostic method for the assessment of the lower respiratory airway health status in horses. Differential cell count and sometimes also total nucleated cell count (TNCC) are routinely measured by time-consuming manual methods, while faster automated methods exist. The aims of this study were to compare: 1) the Sysmex XN-V body fluid (BF) mode with the manual techniques for TNCC and two-part differential into mononuclear and polymorphonuclear cells; 2) the Olympus VS200 slide scanner and software generated deep-learning-based algorithm with manual techniques for four-part differential cell count into alveolar macrophages, lymphocytes, neutrophils, and mast cells. The methods were compared in 69 clinical BAL samples. RESULTS Incorrect gating by the Sysmex BF mode was observed on many scattergrams, therefore all samples were reanalyzed with manually set gates. For the TNCC, a proportional and systematic bias with a correlation of r = 0.79 was seen when comparing the Sysmex BF mode with manual methods. For the two-part differential count, a mild constant and proportional bias and a very small mean difference with moderate limits of agreement with a correlation of r = 0.84 and 0.83 were seen when comparing the Sysmex BF mode with manual methods. The Sysmex BF mode classified significantly more samples as abnormal based on the TNCC and the two-part differential compared to the manual method. When comparing the Olympus VS200 deep-learning-based algorithm with manual methods for the four-part differential cell count, a very small bias in the regression analysis and a very small mean difference in the difference plot, as well as a correlation of r = 0.85 to 0.92 were observed for all four cell categories. The Olympus VS200 deep-learning-based algorithm also showed better precision than manual methods for the four-part differential cell count, especially with an increasing number of analyzed cells. CONCLUSIONS The Sysmex XN-V BF mode can be used for TNCC and two-part differential count measurements after reanalyzing the samples with manually set gates. The Olympus VS200 deep-learning-based algorithm correlates well with the manual methods, while showing better precision and can be used for a four-part differential cell count.
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Affiliation(s)
- Sandra Lapsina
- Clinical Laboratory, Department of Clinical Diagnostics and Services, Vetsuisse Faculty, University of Zurich, Winterthurerstrasse 260, CH-8057, Zurich, Switzerland.
| | - Barbara Riond
- Clinical Laboratory, Department of Clinical Diagnostics and Services, Vetsuisse Faculty, University of Zurich, Winterthurerstrasse 260, CH-8057, Zurich, Switzerland
| | - Regina Hofmann-Lehmann
- Clinical Laboratory, Department of Clinical Diagnostics and Services, Vetsuisse Faculty, University of Zurich, Winterthurerstrasse 260, CH-8057, Zurich, Switzerland
| | - Martina Stirn
- Clinical Laboratory, Department of Clinical Diagnostics and Services, Vetsuisse Faculty, University of Zurich, Winterthurerstrasse 260, CH-8057, Zurich, Switzerland
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4
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Nittala MR, Yang J, Velazquez AE, Salvemini JD, Vance GR, Grady CC, Hathaway B, Roux JA, Vijayakumar S. Precision Population Cancer Medicine in Cancer of the Uterine Cervix: A Potential Roadmap to Eradicate Cervical Cancer. Cureus 2024; 16:e53733. [PMID: 38455773 PMCID: PMC10919943 DOI: 10.7759/cureus.53733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/06/2024] [Indexed: 03/09/2024] Open
Abstract
With the success of the Human Genome Project, the era of genomic medicine (GM) was born. Later on, as GM made progress, there was a feeling of exhilaration that GM could help resolve many disease processes. It also led to the conviction that personalized medicine was possible, and a relatively synonymous word, precision medicine (PM), was coined. However, the influence of environmental factors and social determinants of diseases was only partially given their due importance in the definition of PM, although more recently, this has been recognized. With the rapid advances in GM, big data, data mining, wearable devices for health monitoring, telemedicine, etc., PM can be more easily extended to population-level health care in disease management, prevention, early screening, and so on.and the term precision population medicine (PPM) more aptly describes it. PPM's potential in cancer care was posited earlier,and the current authors planned a series of cancer disease-specific follow-up articles. These papers are mainly aimed at helping emerging students in health sciences (medicine, pharmacy, nursing, dentistry, public health, population health), healthcare management (health-focused business administration, nonprofit administration, public institutional administration, etc.), and policy-making (e.g., political science), although not exclusively. This first disease-specific report focuses on the cancer of the uterine cervix (CC). It describes how recent breakthroughs can be leveraged as force multipliers to improve outcomes in CC - by improving early detection, better screening for CC, potential GM-based interventions during the stage of persistent Human papillomavirus (HPV) infection and treatment interventions - especially among the disadvantaged and resource-scarce populations. This work is a tiny step in our attempts to improve outcomes in CC and ultimately eradicate CC from the face of the earth.
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Affiliation(s)
- Mary R Nittala
- Radiation Oncology, University of Mississippi Medical Center, Jackson, USA
| | - Johnny Yang
- Radiation Oncology, University of Mississippi Medical Center, Jackson, USA
| | | | - John D Salvemini
- Radiation Oncology, University of Mississippi Medical Center, Jackson, USA
| | - Gregory R Vance
- Radiation Oncology, University of Mississippi Medical Center, Jackson, USA
| | - Camille C Grady
- Radiation Oncology, University of Mississippi Medical Center, Jackson, USA
| | - Bradley Hathaway
- Radiation Oncology, University of Mississippi Medical Center, Jackson, USA
| | - Jeffrey A Roux
- Radiation Oncology, University of Mississippi Medical Center, Jackson, USA
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Hu H, Zhang J, Yang T, Hu Q, Yu Y, Huang Q. PATrans: Pixel-Adaptive Transformer for edge segmentation of cervical nuclei on small-scale datasets. Comput Biol Med 2024; 168:107823. [PMID: 38061155 DOI: 10.1016/j.compbiomed.2023.107823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/22/2023] [Accepted: 12/04/2023] [Indexed: 01/10/2024]
Abstract
Transformer has shown excellent performance in various visual tasks, making its application in medicine an inevitable trend. Nevertheless, simply using transformer for small-scale cervical nuclei datasets will result in disastrous performance. Scarce nuclei pixels are not enough to compensate for the lack of CNNs-inherent intrinsic inductive biases, making transformer difficult to model local visual structures and deal with scale variations. Thus, we propose a Pixel Adaptive Transformer(PATrans) to improve the segmentation performance of nuclei edges on small datasets through adaptive pixel tuning. Specifically, to mitigate information loss resulting from mapping different patches into similar latent representations, Consecutive Pixel Patch (CPP) embeds rich multi-scale context into isolated image patches. In this way, it can provide intrinsic scale invariance for 1D input sequences to maintain semantic consistency, allowing the PATrans to establish long-range dependencies quickly. Futhermore, due to the existing handcrafted-attention is agnostic to the widely varying pixel distributions, the Pixel Adaptive Transformer Block (PATB) effectively models the relationships between different pixels across the entire feature map in a data-dependent manner, guided by the important regions. By collaboratively learning local features and global dependencies, PATrans can adaptively reduce the interference of irrelevant pixels. Extensive experiments demonstrate the superiority of our model on three datasets(Ours, ISBI, Herlev).
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Affiliation(s)
- Hexuan Hu
- Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, 211100, PR China; College of Computer and Information, Hohai University, Nanjing, 211100, PR China.
| | - Jianyu Zhang
- Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, 211100, PR China; College of Computer and Information, Hohai University, Nanjing, 211100, PR China.
| | - Tianjin Yang
- Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, 211100, PR China; College of Computer and Information, Hohai University, Nanjing, 211100, PR China.
| | - Qiang Hu
- Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, 211100, PR China; College of Computer and Information, Hohai University, Nanjing, 211100, PR China.
| | - Yufeng Yu
- Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, 211100, PR China; College of Computer and Information, Hohai University, Nanjing, 211100, PR China.
| | - Qian Huang
- Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, 211100, PR China; College of Computer and Information, Hohai University, Nanjing, 211100, PR China.
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6
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Swanson AA, Pantanowitz L. The evolution of cervical cancer screening. J Am Soc Cytopathol 2024; 13:10-15. [PMID: 37865567 DOI: 10.1016/j.jasc.2023.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 09/17/2023] [Accepted: 09/20/2023] [Indexed: 10/23/2023]
Abstract
There are few medical success stories in history as significant as the reduction in cervical cancer incidence. Through the collaborative efforts of dedicated scientific pioneers, the past century has witnessed remarkable advancement that began with the detection of exfoliated cancer cells through cytologic examination to widespread implementation of cervical cancer screening programs to the discovery of the link between cervical cancer and human papillomavirus (HPV). Current screening methods apply HPV-based testing, and artificial intelligence-based screening systems utilizing digitalized cytology images are being used in a continuous effort to optimize the accuracy and efficiency of the Papanicolaou test. This review summarizes the major milestones in cervical cancer screening history to emphasize its evolution as the World Health Organization aims for the global elimination of cervical cancer.
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Affiliation(s)
- Amy A Swanson
- Department of Laboratory Medicine and Pathology, Mayo Clinic Rochester, Rochester, Minnesota.
| | - Liron Pantanowitz
- Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania
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Sambyal D, Sarwar A. Recent developments in cervical cancer diagnosis using deep learning on whole slide images: An Overview of models, techniques, challenges and future directions. Micron 2023; 173:103520. [PMID: 37556898 DOI: 10.1016/j.micron.2023.103520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 07/16/2023] [Accepted: 07/28/2023] [Indexed: 08/11/2023]
Abstract
Integration of whole slide imaging (WSI) and deep learning technology has led to significant improvements in the screening and diagnosis of cervical cancer. WSI enables the examination of all cells on a slide simultaneously and deep learning algorithms can accurately label them as cancerous or non-cancerous. Although many studies have investigated the application of deep learning for diagnosing various diseases, there is a lack of research focusing on the evolution, limitations, and gaps of intelligent algorithms in conjunction with WSI for cervical cancer. This paper provides a comprehensive overview of the state-of-the-art deep learning algorithms used for the timely and precise analysis of cervical WSI images. A total of 115 relevant papers were reviewed, and 37 were selected after screening with specific inclusion and exclusion criteria. Methodological aspects including deep learning techniques, data sources, architectures, and classification techniques employed by the selected studies were analyzed. The review presents the most popular techniques and current trends in deep learning-based cervical classification systems, and categorizes the evolution of the domain based on deep learning techniques, citing an in-depth analysis of various models developed over time. The paper advocates for the implementation of transfer supervised learning when utilizing deep learning models such as ResNet, VGG19, and EfficientNet, and builds a solid foundation for applying relevant techniques in different fields. Although some progress has been made in developing novel models for the diagnosis of cervical cancer, substantial work remains to be done in creating standardized benchmark databases of WSI images for the research community. This paper serves as a comprehensive guide for understanding the fundamental concepts, benefits, and challenges related to various deep learning models on WSI, including their application for cervical system classification. Additionally, it provides valuable insights into future research directions in this area.
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Affiliation(s)
| | - Abid Sarwar
- Department of CS&IT, University of Jammu, India.
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8
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Sekaran K, Varghese RP, Gopikrishnan M, Alsamman AM, El Allali A, Zayed H, Doss C GP. Unraveling the Dysbiosis of Vaginal Microbiome to Understand Cervical Cancer Disease Etiology-An Explainable AI Approach. Genes (Basel) 2023; 14:genes14040936. [PMID: 37107694 PMCID: PMC10137380 DOI: 10.3390/genes14040936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/10/2023] [Accepted: 04/12/2023] [Indexed: 04/29/2023] Open
Abstract
Microbial Dysbiosis is associated with the etiology and pathogenesis of diseases. The studies on the vaginal microbiome in cervical cancer are essential to discern the cause and effect of the condition. The present study characterizes the microbial pathogenesis involved in developing cervical cancer. Relative species abundance assessment identified Firmicutes, Actinobacteria, and Proteobacteria dominating the phylum level. A significant increase in Lactobacillus iners and Prevotella timonensis at the species level revealed its pathogenic influence on cervical cancer progression. The diversity, richness, and dominance analysis divulges a substantial decline in cervical cancer compared to control samples. The β diversity index proves the homogeneity in the subgroups' microbial composition. The association between enriched Lactobacillus iners at the species level, Lactobacillus, Pseudomonas, and Enterococcus genera with cervical cancer is identified by Linear discriminant analysis Effect Size (LEfSe) prediction. The functional enrichment corroborates the microbial disease association with pathogenic infections such as aerobic vaginitis, bacterial vaginosis, and chlamydia. The dataset is trained and validated with repeated k-fold cross-validation technique using a random forest algorithm to determine the discriminative pattern from the samples. SHapley Additive exPlanations (SHAP), a game theoretic approach, is employed to analyze the results predicted by the model. Interestingly, SHAP identified that the increase in Ralstonia has a higher probability of predicting the sample as cervical cancer. New evidential microbiomes identified in the experiment confirm the presence of pathogenic microbiomes in cervical cancer vaginal samples and their mutuality with microbial imbalance.
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Affiliation(s)
- Karthik Sekaran
- School of Biosciences and Technology, Vellore Institute of Technology, Vellore 632014, India
| | | | - Mohanraj Gopikrishnan
- School of Biosciences and Technology, Vellore Institute of Technology, Vellore 632014, India
| | - Alsamman M Alsamman
- Molecular Genetics and Genome Mapping Laboratory, Genome Mapping Department, Agricultural Genetic Engineering Research Institute, Cairo 12619, Egypt
| | - Achraf El Allali
- African Genome Center, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco
| | - Hatem Zayed
- Department of Biomedical Sciences, College of Health Sciences, QU Health, Qatar University, Doha 2713, Qatar
| | - George Priya Doss C
- School of Biosciences and Technology, Vellore Institute of Technology, Vellore 632014, India
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Wang T, Jiang R, Yao Y, Wang Y, Liu W, Qian L, Li J, Weimer J, Huang X. Endometrial Cytology in Diagnosis of Endometrial Cancer: A Systematic Review and Meta-Analysis of Diagnostic Accuracy. J Clin Med 2023; 12:jcm12062358. [PMID: 36983358 PMCID: PMC10054381 DOI: 10.3390/jcm12062358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 02/25/2023] [Accepted: 03/02/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND Because the incidence of endometrial cancer has been increasing every year, it is important to identify an effective screening method for it. The endometrial cytology test (ECT) is considered to be the more acceptable technique compared to invasive endometrial sampling. METHODS The study followed the Priority Reporting Project for Systematic Evaluation and Meta-Analysis (PRISMA-DTA) protocol. This systematic rating searched EMBASE and Web of Science databases for studies on ECT for endometrial cancer from the databases' dates of inception to 30 September 2022. All literature screening and data extraction were performed by two researchers, while the methodological quality of the included studies was assessed against defined inclusion criteria. And a third researcher resolves the disagreements. RESULTS Twenty-six studies were eventually included in this final analysis. Meta-analysis results showed that the diagnostic accuracy characteristics of ECT for endometrial cancer were as follows: combined sensitivity = 0.84 [95% confidence interval (CI) (0.83-0.86)], combined specificity = 0.98 [95% CI (0.98-0.98)], combined positive likelihood ratio = 34.65 [95% CI (20.90-57.45)], combined negative likelihood ratio = 0.21 [95% CI (0.15-0.30)], and area under the summary receiver operating characteristic curve = 0.9673. CONCLUSIONS ECT had the ability to detect endometrial cancer with strong specificity, although some studies have demonstrated significant differences in sensitivity.
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Affiliation(s)
- Ting Wang
- Department of Obstetrics and Gynecology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China
| | - Ruoan Jiang
- Department of Obstetrics and Gynecology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China
| | - Yingsha Yao
- Department of Obstetrics and Gynecology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China
| | - Yaping Wang
- Department of Obstetrics and Gynecology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China
| | - Wu Liu
- Department of Obstetrics and Gynecology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China
| | - Linhua Qian
- Department of Obstetrics and Gynecology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China
| | - Juanqing Li
- Department of Obstetrics and Gynecology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China
| | - Joerg Weimer
- Department of Gynecology and Obstetrics, University Medical Center Schleswig-Holstein, 24103 Kiel, Germany
| | - Xiufeng Huang
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China
- Department of Gynecology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China
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Developing a Tuned Three-Layer Perceptron Fed with Trained Deep Convolutional Neural Networks for Cervical Cancer Diagnosis. Diagnostics (Basel) 2023; 13:diagnostics13040686. [PMID: 36832174 PMCID: PMC9955324 DOI: 10.3390/diagnostics13040686] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 01/14/2023] [Accepted: 02/07/2023] [Indexed: 02/15/2023] Open
Abstract
Cervical cancer is one of the most common types of cancer among women, which has higher death-rate than many other cancer types. The most common way to diagnose cervical cancer is to analyze images of cervical cells, which is performed using Pap smear imaging test. Early and accurate diagnosis can save the lives of many patients and increase the chance of success of treatment methods. Until now, various methods have been proposed to diagnose cervical cancer based on the analysis of Pap smear images. Most of the existing methods can be divided into two groups of methods based on deep learning techniques or machine learning algorithms. In this study, a combination method is presented, whose overall structure is based on a machine learning strategy, where the feature extraction stage is completely separate from the classification stage. However, in the feature extraction stage, deep networks are used. In this paper, a multi-layer perceptron (MLP) neural network fed with deep features is presented. The number of hidden layer neurons is tuned based on four innovative ideas. Additionally, ResNet-34, ResNet-50 and VGG-19 deep networks have been used to feed MLP. In the presented method, the layers related to the classification phase are removed in these two CNN networks, and the outputs feed the MLP after passing through a flatten layer. In order to improve performance, both CNNs are trained on related images using the Adam optimizer. The proposed method has been evaluated on the Herlev benchmark database and has provided 99.23 percent accuracy for the two-classes case and 97.65 percent accuracy for the 7-classes case. The results have shown that the presented method has provided higher accuracy than the baseline networks and many existing methods.
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Chauhan NK, Singh K, Kumar A, Kolambakar SB. HDFCN: A Robust Hybrid Deep Network Based on Feature Concatenation for Cervical Cancer Diagnosis on WSI Pap Smear Slides. BIOMED RESEARCH INTERNATIONAL 2023; 2023:4214817. [PMID: 37101692 PMCID: PMC10125740 DOI: 10.1155/2023/4214817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 01/06/2023] [Accepted: 03/18/2023] [Indexed: 04/28/2023]
Abstract
Cervical cancer is a critical imperilment to a female's health due to its malignancy and fatality rate. The disease can be thoroughly cured by locating and treating the infected tissues in the preliminary phase. The traditional practice for screening cervical cancer is the examination of cervix tissues using the Papanicolaou (Pap) test. Manual inspection of pap smears involves false-negative outcomes due to human error even in the presence of the infected sample. Automated computer vision diagnosis revamps this obstacle and plays a substantial role in screening abnormal tissues affected due to cervical cancer. Here, in this paper, we propose a hybrid deep feature concatenated network (HDFCN) following two-step data augmentation to detect cervical cancer for binary and multiclass classification on the Pap smear images. This network carries out the classification of malignant samples for whole slide images (WSI) of the openly accessible SIPaKMeD database by utilizing the concatenation of features extracted from the fine-tuning of the deep learning (DL) models, namely, VGG-16, ResNet-152, and DenseNet-169, pretrained on the ImageNet dataset. The performance outcomes of the proposed model are compared with the individual performances of the aforementioned DL networks using transfer learning (TL). Our proposed model achieved an accuracy of 97.45% and 99.29% for 5-class and 2-class classifications, respectively. Additionally, the experiment is performed to classify liquid-based cytology (LBC) WSI data containing pap smear images.
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Affiliation(s)
- Nitin Kumar Chauhan
- USIC&T, Guru Gobind Singh Indraprastha University, New Delhi 110078, India
- Department of ECE, Indore Institute of Science & Technology, Indore 453331, India
| | - Krishna Singh
- DSEU Okhla Campus-I, Formerly G. B. Pant Engineering College, New Delhi 110020, India
| | - Amit Kumar
- Department of ECE, Indore Institute of Science & Technology, Indore 453331, India
- Department of Electronics Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India
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Dash S, Sethy PK, Behera SK. Cervical Transformation Zone Segmentation and Classification based on Improved Inception-ResNet-V2 Using Colposcopy Images. Cancer Inform 2023; 22:11769351231161477. [PMID: 37008072 PMCID: PMC10064461 DOI: 10.1177/11769351231161477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 02/16/2023] [Indexed: 03/31/2023] Open
Abstract
The second most frequent malignancy in women worldwide is cervical cancer. In the transformation(transitional) zone, which is a region of the cervix, columnar cells are continuously converting into squamous cells. The most typical location on the cervix for the development of aberrant cells is the transformation zone, a region of transforming cells. This article suggests a 2-phase method that includes segmenting and classifying the transformation zone to identify the type of cervical cancer. In the initial stage, the transformation zone is segmented from the colposcopy images. The segmented images are then subjected to the augmentation process and identified with the improved inception-resnet-v2. Here, multi-scale feature fusion framework that utilizes 3 × 3 convolution kernels from Reduction-A and Reduction-B of inception-resnet-v2 is introduced. The feature extracted from Reduction-A and Reduction -B is concatenated and fed to SVM for classification. This way, the model combines the benefits of residual networks and Inception convolution, increasing network width and resolving the deep network’s training issue. The network can extract several scales of contextual information due to the multi-scale feature fusion, which increases accuracy. The experimental results reveal 81.24% accuracy, 81.24% sensitivity, 90.62% specificity, 87.52% precision, 9.38% FPR, and 81.68% F1 score, 75.27% MCC, and 57.79% Kappa coefficient.
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Affiliation(s)
- Srikanta Dash
- Department of Electronics, Sambalpur University, Sambalpur, Odisha, India
| | - Prabira Kumar Sethy
- Department of Electronics, Sambalpur University, Sambalpur, Odisha, India
- Prabira Kumar Sethy, Department of Electronics, Sambalpur University, Jyoti Vihar, Sambalpur, Odisha 768019, India.
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13
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Moving towards vertically integrated artificial intelligence development. NPJ Digit Med 2022; 5:143. [PMID: 36104535 PMCID: PMC9474277 DOI: 10.1038/s41746-022-00690-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 08/31/2022] [Indexed: 11/08/2022] Open
Abstract
AbstractSubstantial interest and investment in clinical artificial intelligence (AI) research has not resulted in widespread translation to deployed AI solutions. Current attention has focused on bias and explainability in AI algorithm development, external validity and model generalisability, and lack of equity and representation in existing data. While of great importance, these considerations also reflect a model-centric approach seen in published clinical AI research, which focuses on optimising architecture and performance of an AI model on best available datasets. However, even robustly built models using state-of-the-art algorithms may fail once tested in realistic environments due to unpredictability of real-world conditions, out-of-dataset scenarios, characteristics of deployment infrastructure, and lack of added value to clinical workflows relative to cost and potential clinical risks. In this perspective, we define a vertically integrated approach to AI development that incorporates early, cross-disciplinary, consideration of impact evaluation, data lifecycles, and AI production, and explore its implementation in two contrasting AI development pipelines: a scalable “AI factory” (Mayo Clinic, Rochester, United States), and an end-to-end cervical cancer screening platform for resource poor settings (Paps AI, Mbarara, Uganda). We provide practical recommendations for implementers, and discuss future challenges and novel approaches (including a decentralised federated architecture being developed in the NHS (AI4VBH, London, UK)). Growth in global clinical AI research continues unabated, and introduction of vertically integrated teams and development practices can increase the translational potential of future clinical AI projects.
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Karasu Benyes Y, Welch EC, Singhal A, Ou J, Tripathi A. A Comparative Analysis of Deep Learning Models for Automated Cross-Preparation Diagnosis of Multi-Cell Liquid Pap Smear Images. Diagnostics (Basel) 2022; 12:diagnostics12081838. [PMID: 36010189 PMCID: PMC9406372 DOI: 10.3390/diagnostics12081838] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 07/23/2022] [Accepted: 07/27/2022] [Indexed: 11/16/2022] Open
Abstract
Routine Pap smears can facilitate early detection of cervical cancer and improve patient outcomes. The objective of this work is to develop an automated, clinically viable deep neural network for the multi-class Bethesda System diagnosis of multi-cell images in Liquid Pap smear samples. 8 deep learning models were trained on a publicly available multi-class SurePath preparation dataset. This included the 5 best-performing transfer learning models, an ensemble, a novel convolutional neural network (CNN), and a CNN + autoencoder (AE). Additionally, each model was tested on a novel ThinPrep Pap dataset to determine model generalizability across different liquid Pap preparation methods with and without Deep CORAL domain adaptation. All models achieved accuracies >90% when classifying SurePath images. The AE CNN model, 99.80% smaller than the average transfer model, maintained an accuracy of 96.54%. During consecutive training attempts, individual transfer models had high variability in performance, whereas the CNN, AE CNN, and ensemble did not. ThinPrep Pap classification accuracies were notably lower but increased with domain adaptation, with ResNet101 achieving the highest accuracy at 92.65%. This indicates a potential area for future improvement: development of a globally relevant model that can function across different slide preparation methods.
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Affiliation(s)
- Yasmin Karasu Benyes
- Center for Biomedical Engineering, School of Engineering, Brown University, Providence, RI 02912, USA; (Y.K.B.); (E.C.W.)
| | - E. Celeste Welch
- Center for Biomedical Engineering, School of Engineering, Brown University, Providence, RI 02912, USA; (Y.K.B.); (E.C.W.)
| | - Abhinav Singhal
- Department of Computer Science and Engineering, I.I.T. Delhi, Hauz Khas, New Delhi 110016, India;
| | - Joyce Ou
- Department of Pathology and Laboratory Medicine, Alpert Medical School, Brown University, Providence, RI 02912, USA;
| | - Anubhav Tripathi
- Center for Biomedical Engineering, School of Engineering, Brown University, Providence, RI 02912, USA; (Y.K.B.); (E.C.W.)
- Correspondence:
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A Machine-Learning Method to Assess Growth Patterns in Plants of the Family Lemnaceae. PLANTS 2022; 11:plants11151910. [PMID: 35893614 PMCID: PMC9332063 DOI: 10.3390/plants11151910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/19/2022] [Accepted: 07/20/2022] [Indexed: 11/17/2022]
Abstract
Numerous new technologies have been implemented in image analysis methods that help researchers draw scientific conclusions from biological phenomena. Plants of the family Lemnaceae (duckweeds) are the smallest flowering plants in the world, and biometric measurements of single plants and their growth rate are highly challenging. Although the use of software for digital image analysis has changed the way scientists extract phenomenological data (also for studies on duckweeds), the procedure is often not wholly automated and sometimes relies on the intervention of a human operator. Such a constraint can limit the objectivity of the measurements and generally slows down the time required to produce scientific data. Herein lies the need to implement image analysis software with artificial intelligence that can substitute the human operator. In this paper, we present a new method to study the growth rates of the plants of the Lemnaceae family based on the application of machine-learning procedures to digital image analysis. The method is compared to existing analogical and computer-operated procedures. The results showed that our method drastically reduces the time consumption of the human operator while retaining a high correlation in the growth rates measured with other procedures. As expected, machine-learning methods applied to digital image analysis can overcome the constraints of measuring growth rates of very small plants and might help duckweeds gain worldwide attention thanks to their strong nutritional qualities and biological plasticity.
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Wang W, Tian Y, Xu Y, Zhang XX, Li YS, Zhao SF, Bai YH. 3cDe-Net: a cervical cancer cell detection network based on an improved backbone network and multiscale feature fusion. BMC Med Imaging 2022; 22:130. [PMID: 35870877 PMCID: PMC9308346 DOI: 10.1186/s12880-022-00852-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 07/05/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Cervical cancer cell detection is an essential means of cervical cancer screening. However, for thin-prep cytology test (TCT)-based images, the detection accuracies of traditional computer-aided detection algorithms are typically low due to the overlapping of cells with blurred cytoplasmic boundaries. Some typical deep learning-based detection methods, e.g., ResNets and Inception-V3, are not always efficient for cervical images due to the differences between cervical cancer cell images and natural images. As a result, these traditional networks are difficult to directly apply to the clinical practice of cervical cancer screening. METHOD We propose a cervical cancer cell detection network (3cDe-Net) based on an improved backbone network and multiscale feature fusion; the proposed network consists of the backbone network and a detection head. In the backbone network, a dilated convolution and a group convolution are introduced to improve the resolution and expression ability of the model. In the detection head, multiscale features are obtained based on a feature pyramid fusion network to ensure the accurate capture of small cells; then, based on the Faster region-based convolutional neural network (R-CNN), adaptive cervical cancer cell anchors are generated via unsupervised clustering. Furthermore, a new balanced L1-based loss function is defined, which reduces the unbalanced sample contribution loss. RESULT Baselines including ResNet-50, ResNet-101, Inception-v3, ResNet-152 and the feature concatenation network are used on two different datasets (the Data-T and Herlev datasets), and the final quantitative results show the effectiveness of the proposed dilated convolution ResNet (DC-ResNet) backbone network. Furthermore, experiments conducted on both datasets show that the proposed 3cDe-Net, based on the optimal anchors, the defined new loss function, and DC-ResNet, outperforms existing methods and achieves a mean average precision (mAP) of 50.4%. By performing a horizontal comparison of the cells on an image, the category and location information of cancer cells can be obtained concurrently. CONCLUSION The proposed 3cDe-Net can detect cancer cells and their locations on multicell pictures. The model directly processes and analyses samples at the picture level rather than at the cellular level, which is more efficient. In clinical settings, the mechanical workloads of doctors can be reduced, and their focus can be placed on higher-level review work.
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Affiliation(s)
- Wei Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gynecologic Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Yun Tian
- School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China.
| | - Yang Xu
- School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China
| | - Xiao-Xuan Zhang
- School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China
| | - Yan-Song Li
- School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China
| | - Shi-Feng Zhao
- School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China
| | - Yan-Hua Bai
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Pathology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
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Zhou C, Chen F, Li L. A Disintegrin and Metalloprotease 17 (ADAM17)-Modified Bone Marrow Mesenchymal Stem Cells (BMSCs) Enhance Drug-Resistant Cervical Cancer Development. J BIOMATER TISS ENG 2022. [DOI: 10.1166/jbt.2022.3057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
ADAM-17 is a type I transmembrane protein, and its abnormal expression affects the body development and tumor growth. BMSCs act as a target gene carrier in tumor tissues. This study mainly aims to explore the role of ADAM-17 and BMSCs in drug-resistant cervical cancer (CC). BMSCs were
transfected with ADAM-17 or empty vectors and then co-cultured with cisplatin-resistant CC cells followed by analysis of cell morphology. The in vivo effect of ADAM-17-modified BMSC was evaluated using animal model of CC. The protein expression of ADAM-17, EGFR, PI3K, and Akt was detected
using Western blot and RT-qPCR. Transfection of ADAM-17 significantly facilitated tumor growth at different time points (4 d, 7 d, 10 d, 14 d), accompanied with the upregulation of ADAM-17, EGFR, PI3K, and Akt expression (p < 0.05) without differences between empty vector group and
blank group (p > 0.05). Mechanistically, ADAM-17 directly targets EGFR in CC. In conclusion, ADAM-17-modified BMSC enhances the growth of drug-resistant CC cell and tumor growth through EGFR/PI3K/Akt signaling pathway, which may contribute to a novel therapy for treating CC.
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Affiliation(s)
- Chun Zhou
- Department of Obstetrics and Gynecology, Union Jiangnan Hospital, Wuhan, Hubei, 430200, China
| | - Fengxia Chen
- Department of Obstetrics and Gynecology, Union Jiangnan Hospital, Wuhan, Hubei, 430200, China
| | - Liling Li
- Department of Obstetrics and Gynecology, Hubei Provincial Hospital of Integrated Chinese and Western Medicine, Wuhan, Hubei, 430015, China
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Zak J, Grzeszczyk MK, Pater A, Roszkowiak L, Siemion K, Korzynska A. Cell image augmentation for classification task using GANs on Pap smear dataset. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Multi-class nucleus detection and classification using deep convolutional neural network with enhanced high dimensional dissimilarity translation model on cervical cells. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.06.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Ou YC, Tsao TY, Chang MC, Lin YS, Yang WL, Hang JF, Li CB, Lee CM, Yeh CH, Liu TJ. Evaluation of an artificial intelligence algorithm for assisting the Paris System in reporting urinary cytology: A pilot study. Cancer Cytopathol 2022; 130:872-880. [PMID: 35727052 DOI: 10.1002/cncy.22615] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 05/30/2022] [Accepted: 06/02/2022] [Indexed: 11/12/2022]
Abstract
BACKGROUND The Paris System for Reporting Urinary Cytology (TPS) has been shown to improve bladder cancer diagnosis. Advances in artificial intelligence (AI) may assist and improve the clinical workflow by applying TPS in routine diagnostic services. METHODS A deep-learning-based algorithm was developed to identify urothelial cancer candidate cells using whole-slide images (WSIs). In the testing cohort, 131 urine cytology slides were retrospectively retrieved and analyzed using this AI algorithm. The authors compared the performance of one cytopathologist and two cytotechnologists using AI-assisted digital urine cytology. Then, the AI-assisted WSIs were evaluated in the clinical workflow. The cytopathologist first made a diagnosis by reviewing the AI-inferred WSIs and quantitative data (nuclear-to-cytoplasmic ratio and nuclear size) for each sample. After a washout period, the same cytopathologist made a diagnosis for the same samples using direct microscopy. All diagnosis results were compared with the expert panel consensus. RESULTS The AI-assisted diagnosis by the two cytotechnologists and the one cytopathologist demonstrated performance results that were comparable to the expert panel consensus (sensitivity, 79.5% and 82.1% vs. 92.3%, respectively; specificity, 100% and 98.9% vs. 100%, respectively). Furthermore, the performance of the AI-assisted WSIs compared with the microscopic diagnosis by the cytopathologist demonstrated superior sensitivity (92.3% vs. 87.2%) and negative predictive value (96.8% vs. 94.8%). In addition, the AI-assisted reporting demonstrated near perfect agreement with the expert panel consensus (κ = 0.944) and the microscopic diagnosis (κ = 0.862). CONCLUSIONS The AI algorithm developed by the authors effectively assisted TPS-based reporting by providing AI-inferred WSIs and quantitative data.
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Affiliation(s)
- Yen-Chuan Ou
- Division of Urology, Department of Surgery, Tung's Taichung MetroHarbor Hospital, Taichung City, Taiwan
| | - Tang-Yi Tsao
- Department of Pathology, Tung's Taichung MetroHarbor Hospital, Taichung City, Taiwan
| | - Ming-Chen Chang
- Department of Pathology, Tung's Taichung MetroHarbor Hospital, Taichung City, Taiwan
| | - Yi-Sheng Lin
- Division of Urology, Department of Surgery, Tung's Taichung MetroHarbor Hospital, Taichung City, Taiwan
| | | | - Jen-Fan Hang
- Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.,School of Medicine and Institution of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chi-Bin Li
- AIxMed, Inc., Santa Clara, California, USA
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Review of the Standard and Advanced Screening, Staging Systems and Treatment Modalities for Cervical Cancer. Cancers (Basel) 2022; 14:cancers14122913. [PMID: 35740578 PMCID: PMC9220913 DOI: 10.3390/cancers14122913] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/10/2022] [Accepted: 06/10/2022] [Indexed: 12/13/2022] Open
Abstract
Simple Summary This review discusses the timeline and development of the recommended screening tests, diagnosis system, and therapeutics implemented in clinics for precancer and cancer of the uterine cervix. The incorporation of the latest automation, machine learning modules, and state-of-the-art technologies into these aspects are also discussed. Abstract Cancer arising from the uterine cervix is the fourth most common cause of cancer death among women worldwide. Almost 90% of cervical cancer mortality has occurred in low- and middle-income countries. One of the major aetiologies contributing to cervical cancer is the persistent infection by the cancer-causing types of the human papillomavirus. The disease is preventable if the premalignant lesion is detected early and managed effectively. In this review, we outlined the standard guidelines that have been introduced and implemented worldwide for decades, including the cytology, the HPV detection and genotyping, and the immunostaining of surrogate markers. In addition, the staging system used to classify the premalignancy and malignancy of the uterine cervix, as well as the safety and efficacy of the various treatment modalities in clinical trials for cervical cancers, are also discussed. In this millennial world, the advancements in computer-aided technology, including robotic modules and artificial intelligence (AI), are also incorporated into the screening, diagnostic, and treatment platforms. These innovations reduce the dependence on specialists and technologists, as well as the work burden and time incurred for sample processing. However, concerns over the practicality of these advancements remain, due to the high cost, lack of flexibility, and the judgment of a trained professional that is currently not replaceable by a machine.
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Avola D, Bacciu A, Cinque L, Fagioli A, Marini MR, Taiello R. Study on transfer learning capabilities for pneumonia classification in chest-x-rays images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106833. [PMID: 35537296 PMCID: PMC9033299 DOI: 10.1016/j.cmpb.2022.106833] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 04/12/2022] [Accepted: 04/21/2022] [Indexed: 05/09/2023]
Abstract
BACKGROUND over the last year, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and its variants have highlighted the importance of screening tools with high diagnostic accuracy for new illnesses such as COVID-19. In that regard, deep learning approaches have proven as effective solutions for pneumonia classification, especially when considering chest-x-rays images. However, this lung infection can also be caused by other viral, bacterial or fungi pathogens. Consequently, efforts are being poured toward distinguishing the infection source to help clinicians to diagnose the correct disease origin. Following this tendency, this study further explores the effectiveness of established neural network architectures on the pneumonia classification task through the transfer learning paradigm. METHODOLOGY to present a comprehensive comparison, 12 well-known ImageNet pre-trained models were fine-tuned and used to discriminate among chest-x-rays of healthy people, and those showing pneumonia symptoms derived from either a viral (i.e., generic or SARS-CoV-2) or bacterial source. Furthermore, since a common public collection distinguishing between such categories is currently not available, two distinct datasets of chest-x-rays images, describing the aforementioned sources, were combined and employed to evaluate the various architectures. RESULTS the experiments were performed using a total of 6330 images split between train, validation, and test sets. For all models, standard classification metrics were computed (e.g., precision, f1-score), and most architectures obtained significant performances, reaching, among the others, up to 84.46% average f1-score when discriminating the four identified classes. Moreover, execution times, areas under the receiver operating characteristic (AUROC), confusion matrices, activation maps computed via the Grad-CAM algorithm, and additional experiments to assess the robustness of each model using only 50%, 20%, and 10% of the training set were also reported to present an informed discussion on the networks classifications. CONCLUSION this paper examines the effectiveness of well-known architectures on a joint collection of chest-x-rays presenting pneumonia cases derived from either viral or bacterial sources, with particular attention to SARS-CoV-2 contagions for viral pathogens; demonstrating that existing architectures can effectively diagnose pneumonia sources and suggesting that the transfer learning paradigm could be a crucial asset in diagnosing future unknown illnesses.
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Affiliation(s)
- Danilo Avola
- Department of Computer Science, Sapienza University, Via Salaria 113, Rome 00185, Italy
| | - Andrea Bacciu
- Department of Computer Science, Sapienza University, Via Salaria 113, Rome 00185, Italy
| | - Luigi Cinque
- Department of Computer Science, Sapienza University, Via Salaria 113, Rome 00185, Italy
| | - Alessio Fagioli
- Department of Computer Science, Sapienza University, Via Salaria 113, Rome 00185, Italy.
| | - Marco Raoul Marini
- Department of Computer Science, Sapienza University, Via Salaria 113, Rome 00185, Italy
| | - Riccardo Taiello
- Department of Computer Science, Sapienza University, Via Salaria 113, Rome 00185, Italy
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Painuli D, Bhardwaj S, Köse U. Recent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review. Comput Biol Med 2022; 146:105580. [PMID: 35551012 DOI: 10.1016/j.compbiomed.2022.105580] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 04/14/2022] [Accepted: 04/30/2022] [Indexed: 02/07/2023]
Abstract
Being a second most cause of mortality worldwide, cancer has been identified as a perilous disease for human beings, where advance stage diagnosis may not help much in safeguarding patients from mortality. Thus, efforts to provide a sustainable architecture with proven cancer prevention estimate and provision for early diagnosis of cancer is the need of hours. Advent of machine learning methods enriched cancer diagnosis area with its overwhelmed efficiency & low error-rate then humans. A significant revolution has been witnessed in the development of machine learning & deep learning assisted system for segmentation & classification of various cancers during past decade. This research paper includes a review of various types of cancer detection via different data modalities using machine learning & deep learning-based methods along with different feature extraction techniques and benchmark datasets utilized in the recent six years studies. The focus of this study is to review, analyse, classify, and address the recent development in cancer detection and diagnosis of six types of cancers i.e., breast, lung, liver, skin, brain and pancreatic cancer, using machine learning & deep learning techniques. Various state-of-the-art technique are clustered into same group and results are examined through key performance indicators like accuracy, area under the curve, precision, sensitivity, dice score on benchmark datasets and concluded with future research work challenges.
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Affiliation(s)
- Deepak Painuli
- Department of Computer Science and Engineering, Gurukula Kangri Vishwavidyalaya, Haridwar, India.
| | - Suyash Bhardwaj
- Department of Computer Science and Engineering, Gurukula Kangri Vishwavidyalaya, Haridwar, India
| | - Utku Köse
- Department of Computer Engineering, Suleyman Demirel University, Isparta, Turkey
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Chen W, Shen W, Gao L, Li X. Hybrid Loss-Constrained Lightweight Convolutional Neural Networks for Cervical Cell Classification. SENSORS 2022; 22:s22093272. [PMID: 35590961 PMCID: PMC9101629 DOI: 10.3390/s22093272] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 04/11/2022] [Accepted: 04/21/2022] [Indexed: 02/04/2023]
Abstract
Artificial intelligence (AI) technologies have resulted in remarkable achievements and conferred massive benefits to computer-aided systems in medical imaging. However, the worldwide usage of AI-based automation-assisted cervical cancer screening systems is hindered by computational cost and resource limitations. Thus, a highly economical and efficient model with enhanced classification ability is much more desirable. This paper proposes a hybrid loss function with label smoothing to improve the distinguishing power of lightweight convolutional neural networks (CNNs) for cervical cell classification. The results strengthen our confidence in hybrid loss-constrained lightweight CNNs, which can achieve satisfactory accuracy with much lower computational cost for the SIPakMeD dataset. In particular, ShufflenetV2 obtained a comparable classification result (96.18% in accuracy, 96.30% in precision, 96.23% in recall, and 99.08% in specificity) with only one-seventh of the memory usage, one-sixth of the number of parameters, and one-fiftieth of total flops compared with Densenet-121 (96.79% in accuracy). GhostNet achieved an improved classification result (96.39% accuracy, 96.42% precision, 96.39% recall, and 99.09% specificity) with one-half of the memory usage, one-quarter of the number of parameters, and one-fiftieth of total flops compared with Densenet-121 (96.79% in accuracy). The proposed lightweight CNNs are likely to lead to an easily-applicable and cost-efficient automation-assisted system for cervical cancer diagnosis and prevention.
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Dey P. Artificial neural network in diagnostic cytology. Cytojournal 2022; 19:27. [PMID: 35510103 PMCID: PMC9063555 DOI: 10.25259/cytojournal_33_2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 08/28/2021] [Indexed: 11/29/2022] Open
Abstract
The artificial neural network (ANN) is a computer software design or model that simulates the biological neural network of the human brain. Instead of biological neurons, ANN is composed of many layers of nodes that carry the signal and process it to make the final decision. ANN is a modern technology that is widely used in different fields of science. The ANN is reshaping the medical system and the various areas of pathology. In this paper, the basic concept and applications of ANN in cytology have been discussed. In this paper, the various articles published on ANN in the field of cytology have been systemically reviewed. The ANN is relatively less used in cytology. After introducing convolutional neural network and whole slide scanners in the commercial market, it is now essential to have thorough knowledge in this field to start diagnostic application of ANN.
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Brenes D, Barberan CJ, Hunt B, Parra SG, Salcedo MP, Possati-Resende JC, Cremer ML, Castle PE, Fregnani JHTG, Maza M, Schmeler KM, Baraniuk R, Richards-Kortum R. Multi-task network for automated analysis of high-resolution endomicroscopy images to detect cervical precancer and cancer. Comput Med Imaging Graph 2022; 97:102052. [PMID: 35299096 PMCID: PMC9250128 DOI: 10.1016/j.compmedimag.2022.102052] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 02/04/2022] [Accepted: 02/10/2022] [Indexed: 10/19/2022]
Abstract
Cervical cancer is a public health emergency in low- and middle-income countries where resource limitations hamper standard-of-care prevention strategies. The high-resolution endomicroscope (HRME) is a low-cost, point-of-care device with which care providers can image the nuclear morphology of cervical lesions. Here, we propose a deep learning framework to diagnose cervical intraepithelial neoplasia grade 2 or more severe from HRME images. The proposed multi-task convolutional neural network uses nuclear segmentation to learn a diagnostically relevant representation. Nuclear segmentation was trained via proxy labels to circumvent the need for expensive, manually annotated nuclear masks. A dataset of images from over 1600 patients was used to train, validate, and test our algorithm; data from 20% of patients were reserved for testing. An external evaluation set with images from 508 patients was used to further validate our findings. The proposed method consistently outperformed other state-of-the art architectures achieving a test per patient area under the receiver operating characteristic curve (AUC-ROC) of 0.87. Performance was comparable to expert colposcopy with a test sensitivity and specificity of 0.94 (p = 0.3) and 0.58 (p = 1.0), respectively. Patients with recurrent human papillomavirus (HPV) infections are at a higher risk of developing cervical cancer. Thus, we sought to incorporate HPV DNA test results as a feature to inform prediction. We found that incorporating patient HPV status improved test specificity to 0.71 at a sensitivity of 0.94.
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Affiliation(s)
| | | | - Brady Hunt
- Rice University, Houston, TX 77005, USA.
| | | | - Mila P Salcedo
- University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
| | | | | | | | | | - Mauricio Maza
- Basic Health International, San Savlador, El Salvador.
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Pantanowitz L. Improving the Pap test with artificial intelligence. Cancer Cytopathol 2022; 130:402-404. [PMID: 35291050 DOI: 10.1002/cncy.22561] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 01/31/2022] [Indexed: 11/07/2022]
Affiliation(s)
- Liron Pantanowitz
- Department of Pathology, University of Michigan, Ann Arbor, Michigan
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Hou X, Shen G, Zhou L, Li Y, Wang T, Ma X. Artificial Intelligence in Cervical Cancer Screening and Diagnosis. Front Oncol 2022; 12:851367. [PMID: 35359358 PMCID: PMC8963491 DOI: 10.3389/fonc.2022.851367] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 02/10/2022] [Indexed: 12/11/2022] Open
Abstract
Cervical cancer remains a leading cause of cancer death in women, seriously threatening their physical and mental health. It is an easily preventable cancer with early screening and diagnosis. Although technical advancements have significantly improved the early diagnosis of cervical cancer, accurate diagnosis remains difficult owing to various factors. In recent years, artificial intelligence (AI)-based medical diagnostic applications have been on the rise and have excellent applicability in the screening and diagnosis of cervical cancer. Their benefits include reduced time consumption, reduced need for professional and technical personnel, and no bias owing to subjective factors. We, thus, aimed to discuss how AI can be used in cervical cancer screening and diagnosis, particularly to improve the accuracy of early diagnosis. The application and challenges of using AI in the diagnosis and treatment of cervical cancer are also discussed.
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Affiliation(s)
- Xin Hou
- Department of Obstetrics and Gynecology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Guangyang Shen
- Department of Obstetrics and Gynecology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Liqiang Zhou
- Cancer Centre and Center of Reproduction, Development and Aging, Faculty of Health Sciences, University of Macau, Macau, Macau SAR, China
| | - Yinuo Li
- Department of Obstetrics and Gynecology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Tian Wang
- Department of Obstetrics and Gynecology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Xiangyi Ma
- Department of Obstetrics and Gynecology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Xiangyi Ma,
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Predictions of cervical cancer identification by photonic method combined with machine learning. Sci Rep 2022; 12:3762. [PMID: 35260666 PMCID: PMC8904553 DOI: 10.1038/s41598-022-07723-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 02/15/2022] [Indexed: 12/26/2022] Open
Abstract
Cervical cancer is one of the most commonly appearing cancers, which early diagnosis is of greatest importance. Unfortunately, many diagnoses are based on subjective opinions of doctors-to date, there is no general measurement method with a calibrated standard. The problem can be solved with the measurement system being a fusion of an optoelectronic sensor and machine learning algorithm to provide reliable assistance for doctors in the early diagnosis stage of cervical cancer. We demonstrate the preliminary research on cervical cancer assessment utilizing an optical sensor and a prediction algorithm. Since each matter is characterized by refractive index, measuring its value and detecting changes give information about the state of the tissue. The optical measurements provided datasets for training and validating the analyzing software. We present data preprocessing, machine learning results utilizing four algorithms (Random Forest, eXtreme Gradient Boosting, Naïve Bayes, Convolutional Neural Networks) and assessment of their performance for classification of tissue as healthy or sick. Our solution allows for rapid sample measurement and automatic classification of the results constituting a potential support tool for doctors.
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Deep learning in image-based breast and cervical cancer detection: a systematic review and meta-analysis. NPJ Digit Med 2022; 5:19. [PMID: 35169217 PMCID: PMC8847584 DOI: 10.1038/s41746-022-00559-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 12/22/2021] [Indexed: 12/15/2022] Open
Abstract
Accurate early detection of breast and cervical cancer is vital for treatment success. Here, we conduct a meta-analysis to assess the diagnostic performance of deep learning (DL) algorithms for early breast and cervical cancer identification. Four subgroups are also investigated: cancer type (breast or cervical), validation type (internal or external), imaging modalities (mammography, ultrasound, cytology, or colposcopy), and DL algorithms versus clinicians. Thirty-five studies are deemed eligible for systematic review, 20 of which are meta-analyzed, with a pooled sensitivity of 88% (95% CI 85–90%), specificity of 84% (79–87%), and AUC of 0.92 (0.90–0.94). Acceptable diagnostic performance with analogous DL algorithms was highlighted across all subgroups. Therefore, DL algorithms could be useful for detecting breast and cervical cancer using medical imaging, having equivalent performance to human clinicians. However, this tentative assertion is based on studies with relatively poor designs and reporting, which likely caused bias and overestimated algorithm performance. Evidence-based, standardized guidelines around study methods and reporting are required to improve the quality of DL research.
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Abstract
Cervical cancer is one of the leading causes of premature mortality among women worldwide and more than 85% of these deaths are in developing countries. There are several risk factors associated with cervical cancer. In this paper, we developed a predictive model for predicting the outcome of patients with cervical cancer, given risk patterns from individual medical records and preliminary screening. This work presents a decision tree (DT) classification algorithm to analyze the risk factors of cervical cancer. Recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO) feature selection techniques were fully explored to determine the most important attributes for cervical cancer prediction. The dataset employed here contains missing values and is highly imbalanced. Therefore, a combination of under and oversampling techniques called SMOTETomek was employed. A comparative analysis of the proposed model has been performed to show the effectiveness of feature selection and class imbalance based on the classifier’s accuracy, sensitivity, and specificity. The DT with the selected features from RFE and SMOTETomek has better results with an accuracy of 98.72% and sensitivity of 100%. DT classifier is shown to have better performance in handling classification problems when the features are reduced, and the problem of high class imbalance is addressed.
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Zhao C, Shuai R, Ma L, Liu W, Wu M. Improving cervical cancer classification with imbalanced datasets combining taming transformers with T2T-ViT. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:24265-24300. [PMID: 35342326 PMCID: PMC8933771 DOI: 10.1007/s11042-022-12670-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 01/12/2022] [Accepted: 02/21/2022] [Indexed: 05/12/2023]
Abstract
UNLABELLED Cervical cell classification has important clinical significance in cervical cancer screening at early stages. However, there are fewer public cervical cancer smear cell datasets, the weights of each classes' samples are unbalanced, the image quality is uneven, and the classification research results based on CNN tend to overfit. To solve the above problems, we propose a cervical cell image generation model based on taming transformers (CCG-taming transformers) to provide high-quality cervical cancer datasets with sufficient samples and balanced weights, we improve the encoder structure by introducing SE-block and MultiRes-block to improve the ability to extract information from cervical cancer cells images; we introduce Layer Normlization to standardize the data, which is convenient for the subsequent non-linear processing of the data by the ReLU activation function in feed forward; we also introduce SMOTE-Tomek Links to balance the source data set and the number of samples and weights of the images we use Tokens-to-Token Vision Transformers (T2T-ViT) combing transfer learning to classify the cervical cancer smear cell image dataset to improve the classification performance. Classification experiments using the model proposed in this paper are performed on three public cervical cancer datasets, the classification accuracy in the liquid-based cytology Pap smear dataset (4-class), SIPAKMeD (5-class), and Herlev (7-class) are 98.79%, 99.58%, and 99.88%, respectively. The quality of the images we generated on these three data sets is very close to the source data set, the final averaged inception score (IS), Fréchet inception distance (FID), Recall and Precision are 3.75, 0.71, 0.32 and 0.65 respectively. Our method improves the accuracy of cervical cancer smear cell classification, provides more cervical cell sample images for cervical cancer-related research, and assists gynecologists to judge and diagnose different types of cervical cancer cells and analyze cervical cancer cells at different stages, which are difficult to distinguish. This paper applies the transformer to the generation and recognition of cervical cancer cell images for the first time. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s11042-022-12670-0.
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Affiliation(s)
- Chen Zhao
- College of Computer Science and Technology, Nanjing Tech University, Nanjing, 211816 China
| | - Renjun Shuai
- College of Computer Science and Technology, Nanjing Tech University, Nanjing, 211816 China
| | - Li Ma
- Nanjing Health Information Center, Nanjing, 210003 China
| | - Wenjia Liu
- Changzhou No. 2 People’s Hospital affiliated with Nanjing Medical University, Changzhou, 213003 China
| | - Menglin Wu
- College of Computer Science and Technology, Nanjing Tech University, Nanjing, 211816 China
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Huong AKC, Tay KG, Ngu XTI. Five-Class Classification of Cervical Pap Smear Images: A Study of CNN-Error-Correcting SVM Models. Healthc Inform Res 2021; 27:298-306. [PMID: 34788910 PMCID: PMC8654336 DOI: 10.4258/hir.2021.27.4.298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 07/23/2021] [Indexed: 12/03/2022] Open
Abstract
Objectives Different complex strategies of fusing handcrafted descriptors and features from convolutional neural network (CNN) models have been studied, mainly for two-class Papanicolaou (Pap) smear image classification. This paper explores a simplified system using combined binary coding for a five-class version of this problem. Methods This system extracted features from transfer learning of AlexNet, VGG19, and ResNet50 networks before reducing this problem into multiple binary sub-problems using error-correcting coding. The learners were trained using the support vector machine (SVM) method. The outputs of these classifiers were combined and compared to the true class codes for the final prediction. Results Despite the superior performance of VGG19-SVM, with mean ± standard deviation accuracy and sensitivity of 80.68% ± 2.00% and 80.86% ± 0.45%, respectively, this model required a long training time. There were also false-negative cases using both the VGGNet-SVM and ResNet-SVM models. AlexNet-SVM was more efficient in terms of running speed and prediction consistency. Our findings also showed good diagnostic ability, with an area under the curve of approximately 0.95. Further investigation also showed good agreement between our research outcomes and that of the state-of-the-art methods, with specificity ranging from 93% to 100%. Conclusions We believe that the AlexNet-SVM model can be conveniently applied for clinical use. Further research could include the implementation of an optimization algorithm for hyperparameter tuning, as well as an appropriate selection of experimental design to improve the efficiency of Pap smear image classification.
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Affiliation(s)
- Audrey K C Huong
- Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia
| | - Kim Gaik Tay
- Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia
| | - Xavier T I Ngu
- Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia
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Oyamada Y, Ozuru R, Masuzawa T, Miyahara S, Nikaido Y, Obata F, Saito M, Villanueva SYAM, Fujii J. A machine learning model of microscopic agglutination test for diagnosis of leptospirosis. PLoS One 2021; 16:e0259907. [PMID: 34784387 PMCID: PMC8594833 DOI: 10.1371/journal.pone.0259907] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 10/28/2021] [Indexed: 11/21/2022] Open
Abstract
Leptospirosis is a zoonosis caused by the pathogenic bacterium Leptospira. The Microscopic Agglutination Test (MAT) is widely used as the gold standard for diagnosis of leptospirosis. In this method, diluted patient serum is mixed with serotype-determined Leptospires, and the presence or absence of aggregation is determined under a dark-field microscope to calculate the antibody titer. Problems of the current MAT method are 1) a requirement of examining many specimens per sample, and 2) a need of distinguishing contaminants from true aggregates to accurately identify positivity. Therefore, increasing efficiency and accuracy are the key to refine MAT. It is possible to achieve efficiency and standardize accuracy at the same time by automating the decision-making process. In this study, we built an automatic identification algorithm of MAT using a machine learning method to determine agglutination within microscopic images. The machine learned the features from 316 positive and 230 negative MAT images created with sera of Leptospira-infected (positive) and non-infected (negative) hamsters, respectively. In addition to the acquired original images, wavelet-transformed images were also considered as features. We utilized a support vector machine (SVM) as a proposed decision method. We validated the trained SVMs with 210 positive and 154 negative images. When the features were obtained from original or wavelet-transformed images, all negative images were misjudged as positive, and the classification performance was very low with sensitivity of 1 and specificity of 0. In contrast, when the histograms of wavelet coefficients were used as features, the performance was greatly improved with sensitivity of 0.99 and specificity of 0.99. We confirmed that the current algorithm judges the positive or negative of agglutinations in MAT images and gives the further possibility of automatizing MAT procedure.
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Affiliation(s)
- Yuji Oyamada
- Department of Electrical Engineering and Computer Science, Faculty of Engineering, Tottori University, Tottori, Japan
| | - Ryo Ozuru
- Division of Bacteriology, Department of Microbiology and Immunology, Faculty of Medicine, Tottori University, Yonago, Tottori, Japan
- * E-mail:
| | - Toshiyuki Masuzawa
- Laboratory of Microbiology and Immunology, Faculty of Pharmaceutical Sciences, Chiba Institute of Science, Choshi, Chiba, Japan
| | - Satoshi Miyahara
- Department of Microbiology, School of Medicine, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
| | - Yasuhiko Nikaido
- Department of Microbiology, School of Medicine, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
| | - Fumiko Obata
- Division of Bacteriology, Department of Microbiology and Immunology, Faculty of Medicine, Tottori University, Yonago, Tottori, Japan
| | - Mitsumasa Saito
- Department of Microbiology, School of Medicine, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
| | | | - Jun Fujii
- Division of Bacteriology, Department of Microbiology and Immunology, Faculty of Medicine, Tottori University, Yonago, Tottori, Japan
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Drokow EK, Baffour AA, Effah CY, Agboyibor C, Akpabla GS, Sun K. Building a predictive model to assist in the diagnosis of cervical cancer. Future Oncol 2021; 18:67-84. [PMID: 34729999 DOI: 10.2217/fon-2021-0767] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Aim: Cervical cancer is still one of the most common gynecologic cancers in the world. Since cervical cancer is a potentially preventive cancer, earlier detection is the most effective technique for decreasing the worldwide incidence of the illness. Materials and methods: This research presents a novel ensemble technique for predicting cervical cancer risk. Specifically, the authors introduce a voting classifier that aggregates prediction probabilities from multiple machine-learning models: logistic regression, K-nearest neighbor, decision tree, XGBoost and multilayer perceptron. Results: The average accuracy, precision, recall and f1-score of the voting classifier were 96.6, 97.4, 95.9 and 96.6, respectively. Furthermore, the voting algorithm gains average high values for all evaluation metrics (accuracy, precision, recall and f1-score). The f1-score of the algorithm is 96%, which demonstrates the robustness of the model. Conclusion: The findings suggest that the probability of having cervical cancer can be accurately predicted utilizing the voting technique.
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Affiliation(s)
- Emmanuel Kwateng Drokow
- Department of Radiation Oncology, Zhengzhou University People's Hospital & Henan Provincial People's Hospital, Henan, China
| | - Adu Asare Baffour
- School of Information & Software Engineering, University of Electronic Science & Technology of China, 610054, China
| | | | - Clement Agboyibor
- School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
| | | | - Kai Sun
- Department of Haematology, Zhengzhou University People's Hospital & Henan Provincial People's Hospital Henan, China
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Deep Learning for Intelligent Recognition and Prediction of Endometrial Cancer. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:1148309. [PMID: 34484650 PMCID: PMC8413058 DOI: 10.1155/2021/1148309] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Accepted: 07/29/2021] [Indexed: 01/10/2023]
Abstract
The aim of the study was to investigate the intelligent recognition of radiomics based on the convolutional neural network (CNN) in predicting endometrial cancer (EC). In this study, 158 patients with EC in hospital were selected as the research objects and divided into a training group and a test group. All the patients underwent magnetic resonance imaging (MRI) before surgery. Based on the CNN, the imaging model of EC prediction was constructed according to the characteristics. Besides, the comprehensive prediction model was established through the clinical information and imaging parameters. The results showed that the area under the working characteristic curve (AUC) of the radiomics model and comprehensive prediction model was 0.897 and 0.913 in the training group, respectively. In addition, the AUC of the radiomics model was 0.889 in the test group and that of the comprehensive prediction model was 0.897. The comprehensive prediction model was established through specific imaging parameters and clinical pathological information, and its prediction performance was good, indicating that radiomics parameters could be applied as noninvasive markers to predict EC.
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38
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Manna A, Kundu R, Kaplun D, Sinitca A, Sarkar R. A fuzzy rank-based ensemble of CNN models for classification of cervical cytology. Sci Rep 2021; 11:14538. [PMID: 34267261 PMCID: PMC8282795 DOI: 10.1038/s41598-021-93783-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Accepted: 06/30/2021] [Indexed: 12/14/2022] Open
Abstract
Cervical cancer affects more than 0.5 million women annually causing more than 0.3 million deaths. Detection of cancer in its early stages is of prime importance for eradicating the disease from the patient’s body. However, regular population-wise screening of cancer is limited by its expensive and labour intensive detection process, where clinicians need to classify individual cells from a stained slide consisting of more than 100,000 cervical cells, for malignancy detection. Thus, Computer-Aided Diagnosis (CAD) systems are used as a viable alternative for easy and fast detection of cancer. In this paper, we develop such a method where we form an ensemble-based classification model using three Convolutional Neural Network (CNN) architectures, namely Inception v3, Xception and DenseNet-169 pre-trained on ImageNet dataset for Pap stained single cell and whole-slide image classification. The proposed ensemble scheme uses a fuzzy rank-based fusion of classifiers by considering two non-linear functions on the decision scores generated by said base learners. Unlike the simple fusion schemes that exist in the literature, the proposed ensemble technique makes the final predictions on the test samples by taking into consideration the confidence in the predictions of the base classifiers. The proposed model has been evaluated on two publicly available benchmark datasets, namely, the SIPaKMeD Pap Smear dataset and the Mendeley Liquid Based Cytology (LBC) dataset, using a 5-fold cross-validation scheme. On the SIPaKMeD Pap Smear dataset, the proposed framework achieves a classification accuracy of 98.55% and sensitivity of 98.52% in its 2-class setting, and 95.43% accuracy and 98.52% sensitivity in its 5-class setting. On the Mendeley LBC dataset, the accuracy achieved is 99.23% and sensitivity of 99.23%. The results obtained outperform many of the state-of-the-art models, thereby justifying the effectiveness of the same. The relevant codes of this proposed model are publicly available on GitHub.
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Affiliation(s)
- Ankur Manna
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India
| | - Rohit Kundu
- Department of Electrical Engineering, Jadavpur University, Kolkata, 700032, India
| | - Dmitrii Kaplun
- Department of Automation and Control Processes, Saint Petersburg Electrotechnical University "LETI", Saint Petersburg, 197376, Russian Federation.
| | - Aleksandr Sinitca
- Department of Automation and Control Processes, Saint Petersburg Electrotechnical University "LETI", Saint Petersburg, 197376, Russian Federation
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India
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N. Diniz D, T. Rezende M, G. C. Bianchi A, M. Carneiro C, J. S. Luz E, J. P. Moreira G, M. Ushizima D, N. S. de Medeiros F, J. F. Souza M. A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification. J Imaging 2021. [PMCID: PMC8321382 DOI: 10.3390/jimaging7070111] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
In recent years, deep learning methods have outperformed previous state-of-the-art machine learning techniques for several problems, including image classification. Classifying cells in Pap smear images is very challenging, and it is still of paramount importance for cytopathologists. The Pap test is a cervical cancer prevention test that tracks preneoplastic changes in cervical epithelial cells. Carrying out this exam is important in that early detection. It is directly related to a greater chance of curing or reducing the number of deaths caused by the disease. The analysis of Pap smears is exhaustive and repetitive, as it is performed manually by cytopathologists. Therefore, a tool that assists cytopathologists is needed. This work considers 10 deep convolutional neural networks and proposes an ensemble of the three best architectures to classify cervical cancer upon cell nuclei and reduce the professionals’ workload. The dataset used in the experiments is available in the Center for Recognition and Inspection of Cells (CRIC) Searchable Image Database. Considering the metrics of precision, recall, F1-score, accuracy, and sensitivity, the proposed ensemble improves previous methods shown in the literature for two- and three-class classification. We also introduce the six-class classification outcome.
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Affiliation(s)
- Débora N. Diniz
- Departamento de Computação, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil; (A.G.C.B.); (E.J.S.L.); (G.J.P.M.); (M.J.F.S.)
- Correspondence:
| | - Mariana T. Rezende
- Departamento de Análises Clínicas, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil; (M.T.R.); (C.M.C.)
| | - Andrea G. C. Bianchi
- Departamento de Computação, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil; (A.G.C.B.); (E.J.S.L.); (G.J.P.M.); (M.J.F.S.)
| | - Claudia M. Carneiro
- Departamento de Análises Clínicas, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil; (M.T.R.); (C.M.C.)
| | - Eduardo J. S. Luz
- Departamento de Computação, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil; (A.G.C.B.); (E.J.S.L.); (G.J.P.M.); (M.J.F.S.)
| | - Gladston J. P. Moreira
- Departamento de Computação, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil; (A.G.C.B.); (E.J.S.L.); (G.J.P.M.); (M.J.F.S.)
| | - Daniela M. Ushizima
- Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA;
- Berkeley Institute for Data Science, University of California, Berkeley, CA 94720, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA 94143, USA
| | - Fátima N. S. de Medeiros
- Departamento de Engenharia de Teleinformática, Universidade Federal do Ceará (UFC), Fortaleza 60455-970, Brazil;
| | - Marcone J. F. Souza
- Departamento de Computação, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil; (A.G.C.B.); (E.J.S.L.); (G.J.P.M.); (M.J.F.S.)
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Elakkiya R, Subramaniyaswamy V, Vijayakumar V, Mahanti A. Cervical Cancer Diagnostics Healthcare System Using Hybrid Object Detection Adversarial Networks. IEEE J Biomed Health Inform 2021; 26:1464-1471. [PMID: 34214045 DOI: 10.1109/jbhi.2021.3094311] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Cervical cancer is one of the common cancers among women and it causes significant mortality in many developing countries. Diagnosis of cervical lesions is done using pap smear test or visual inspection using acetic acid (staining). Digital colposcopy, an inexpensive methodology, provides painless and efficient screening results. Therefore, automating cervical cancer screening using colposcopy images will be highly useful in saving many lives. Nowadays, many automation techniques using computer vision and machine learning in cervical screening gained attention, paving the way for diagnosing cervical cancer. However, most of the methods rely entirely on the annotation of cervical spotting and segmentation. This paper aims to introduce the Faster Small-Object Detection Neural Networks (FSOD-GAN) to address the cervical screening and diagnosis of cervical cancer and the type of cancer using digital colposcopy images. The proposed approach automatically detects the cervical spot using Faster Region-Based Convolutional Neural Network (FR-CNN) and performs the hierarchical multiclass classification of three types of cervical cancer lesions. Experimentation was done with colposcopy data collected from available open sources consisting of 1,993 patients with three cervical categories, and the proposed approach shows 99% accuracy in diagnosing the stages of cervical cancer.
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41
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Liu J, Liang T, Peng Y, Peng G, Sun L, Li L, Dong H. Segmentation of acetowhite region in uterine cervical image based on deep learning. Technol Health Care 2021; 30:469-482. [PMID: 34180439 DOI: 10.3233/thc-212890] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
BACKGROUND Acetowhite (AW) region is a critical physiological phenomenon of precancerous lesions of cervical cancer. An accurate segmentation of the AW region can provide a useful diagnostic tool for gynecologic oncologists in screening cervical cancers. Traditional approaches for the segmentation of AW regions relied heavily on manual or semi-automatic methods. OBJECTIVE To automatically segment the AW regions from colposcope images. METHODS First, the cervical region was extracted from the original colposcope images by k-means clustering algorithm. Second, a deep learning-based image semantic segmentation model named DeepLab V3+ was used to segment the AW region from the cervical image. RESULTS The results showed that, compared to the fuzzy clustering segmentation algorithm and the level set segmentation algorithm, the new method proposed in this study achieved a mean Jaccard Index (JI) accuracy of 63.6% (improved by 27.9% and 27.5% respectively), a mean specificity of 94.9% (improved by 55.8% and 32.3% respectively) and a mean accuracy of 91.2% (improved by 38.6% and 26.4% respectively). A mean sensitivity of 78.2% was achieved by the proposed method, which was 17.4% and 10.1% lower respectively. Compared to the image semantic segmentation models U-Net and PSPNet, the proposed method yielded a higher mean JI accuracy, mean sensitivity and mean accuracy. CONCLUSION The improved segmentation performance suggested that the proposed method may serve as a useful complimentary tool in screening cervical cancer.
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Affiliation(s)
- Jun Liu
- Department of Information Engineering, Nanchang Hangkong University, Nanchang, Jiangxi 330036, China
| | - Tong Liang
- Department of Information Engineering, Nanchang Hangkong University, Nanchang, Jiangxi 330036, China
| | - Yun Peng
- San Diego, California, CA 91355, USA
| | - Gengyou Peng
- Department of Information Engineering, Nanchang Hangkong University, Nanchang, Jiangxi 330036, China
| | - Lechan Sun
- Department of Information Engineering, Nanchang Hangkong University, Nanchang, Jiangxi 330036, China
| | - Ling Li
- Department of Gynecologic Oncology, Jiangxi Maternal and Child Health Hospital, Jiangxi 330006, China
| | - Hua Dong
- Department of Information Engineering, Nanchang Hangkong University, Nanchang, Jiangxi 330036, China
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Cric searchable image database as a public platform for conventional pap smear cytology data. Sci Data 2021; 8:151. [PMID: 34112812 PMCID: PMC8192784 DOI: 10.1038/s41597-021-00933-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 05/11/2021] [Indexed: 01/02/2023] Open
Abstract
Amidst the current health crisis and social distancing, telemedicine has become an important part of mainstream of healthcare, and building and deploying computational tools to support screening more efficiently is an increasing medical priority. The early identification of cervical cancer precursor lesions by Pap smear test can identify candidates for subsequent treatment. However, one of the main challenges is the accuracy of the conventional method, often subject to high rates of false negative. While machine learning has been highlighted to reduce the limitations of the test, the absence of high-quality curated datasets has prevented strategies development to improve cervical cancer screening. The Center for Recognition and Inspection of Cells (CRIC) platform enables the creation of CRIC Cervix collection, currently with 400 images (1,376 × 1,020 pixels) curated from conventional Pap smears, with manual classification of 11,534 cells. This collection has the potential to advance current efforts in training and testing machine learning algorithms for the automation of tasks as part of the cytopathological analysis in the routine work of laboratories.
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Kaushik M, Chandra Joshi R, Kushwah AS, Gupta MK, Banerjee M, Burget R, Dutta MK. Cytokine gene variants and socio-demographic characteristics as predictors of cervical cancer: A machine learning approach. Comput Biol Med 2021; 134:104559. [PMID: 34147008 DOI: 10.1016/j.compbiomed.2021.104559] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 05/30/2021] [Accepted: 06/04/2021] [Indexed: 01/03/2023]
Abstract
Cervical cancer is still one of the most prevalent cancers in women and a significant cause of mortality. Cytokine gene variants and socio-demographic characteristics have been reported as biomarkers for determining the cervical cancer risk in the Indian population. This study was designed to apply a machine learning-based model using these risk factors for better prognosis and prediction of cervical cancer. This study includes the dataset of cytokine gene variants, clinical and socio-demographic characteristics of normal healthy control subjects, and cervical cancer cases. Different risk factors, including demographic details and cytokine gene variants, were analysed using different machine learning approaches. Various statistical parameters were used for evaluating the proposed method. After multi-step data processing and random splitting of the dataset, machine learning methods were applied and evaluated with 5-fold cross-validation and also tested on the unseen data records of a collected dataset for proper evaluation and analysis. The proposed approaches were verified after analysing various performance metrics. The logistic regression technique achieved the highest average accuracy of 82.25% and the highest average F1-score of 82.58% among all the methods. Ridge classifiers and the Gaussian Naïve Bayes classifier achieved the highest sensitivity-85%. The ridge classifier surpasses most of the machine learning classifiers with 84.78% accuracy and 97.83% sensitivity. The risk factors analysed in this study can be taken as biomarkers in developing a cervical cancer diagnosis system. The outcomes demonstrate that the machine learning assisted analysis of cytokine gene variants and socio-demographic characteristics can be utilised effectively for predicting the risk of developing cervical cancer.
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Affiliation(s)
- Manoj Kaushik
- Centre for Advanced Studies, Dr. A. P. J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India
| | - Rakesh Chandra Joshi
- Centre for Advanced Studies, Dr. A. P. J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India
| | - Atar Singh Kushwah
- Molecular & Human Genetics Laboratory, Department of Zoology, University of Lucknow, Lucknow, Uttar Pradesh, India; Department of Zoology, Institute of Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India
| | - Maneesh Kumar Gupta
- Molecular & Human Genetics Laboratory, Department of Zoology, University of Lucknow, Lucknow, Uttar Pradesh, India
| | - Monisha Banerjee
- Molecular & Human Genetics Laboratory, Department of Zoology, University of Lucknow, Lucknow, Uttar Pradesh, India
| | - Radim Burget
- Brno University of Technology, Faculty of Electrical Engineering, Brno, Czech Republic
| | - Malay Kishore Dutta
- Centre for Advanced Studies, Dr. A. P. J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India.
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Liang Y, Pan C, Sun W, Liu Q, Du Y. Global context-aware cervical cell detection with soft scale anchor matching. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 204:106061. [PMID: 33819821 DOI: 10.1016/j.cmpb.2021.106061] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 03/18/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Computer-aided cervical cancer screening based on an automated recognition of cervical cells has the potential to significantly reduce error rate and increase productivity compared to manual screening. Traditional methods often rely on the success of accurate cell segmentation and discriminative hand-crafted features extraction. Recently, detector based on convolutional neural network is applied to reduce the dependency on hand-crafted features and eliminate the necessary segmentation. However, these methods tend to yield too much false positive predictions. METHODS This paper proposes a global context-aware framework to deal with this problem, which integrates global context information by an image-level classification branch and a weighted loss. And the prediction of this branch is merged into cell detection for filtering false positive predictions. Furthermore, a new ground truth assignment strategy in the feature pyramid called soft scale anchor matching is proposed, which matches ground truths with anchors across scales softly. This strategy searches the most appropriate representation of ground truths in each layer and add more positive samples with different scales, which facilitate the feature learning. RESULTS Our proposed methods finally get 5.7% increase in mean average precision and 18.5% increase in specificity with sacrifice of 2.6% delay in inference time. CONCLUSIONS Our proposed methods which totally avoid the dependence on segmentation of cervical cells, show the great potential to reduce the workload for pathologists in automation-assisted cervical cancer screening.
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Affiliation(s)
- Yixiong Liang
- School of Computer Science and Engineering, Central South University, Changsha, China.
| | - Changli Pan
- School of Computer Science and Engineering, Central South University, Changsha, China.
| | - Wanxin Sun
- School of Computer Science and Engineering, Central South University, Changsha, China.
| | - Qing Liu
- School of Computer Science and Engineering, Central South University, Changsha, China.
| | - Yun Du
- The Fourth Hospital of Hebei Medical University, Hebei Province China-Japan Friendship Center for Cancer Detection, China.
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Victória Matias A, Atkinson Amorim JG, Buschetto Macarini LA, Cerentini A, Casimiro Onofre AS, De Miranda Onofre FB, Daltoé FP, Stemmer MR, von Wangenheim A. What is the state of the art of computer vision-assisted cytology? A Systematic Literature Review. Comput Med Imaging Graph 2021; 91:101934. [PMID: 34174544 DOI: 10.1016/j.compmedimag.2021.101934] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 04/16/2021] [Accepted: 05/04/2021] [Indexed: 11/28/2022]
Abstract
Cytology is a low-cost and non-invasive diagnostic procedure employed to support the diagnosis of a broad range of pathologies. Cells are harvested from tissues by aspiration or scraping, and it is still predominantly performed manually by medical or laboratory professionals extensively trained for this purpose. It is a time-consuming and repetitive process where many diagnostic criteria are subjective and vulnerable to human interpretation. Computer Vision technologies, by automatically generating quantitative and objective descriptions of examinations' contents, can help minimize the chances of misdiagnoses and shorten the time required for analysis. To identify the state-of-art of computer vision techniques currently applied to cytology, we conducted a Systematic Literature Review, searching for approaches for the segmentation, detection, quantification, and classification of cells and organelles using computer vision on cytology slides. We analyzed papers published in the last 4 years. The initial search was executed in September 2020 and resulted in 431 articles. After applying the inclusion/exclusion criteria, 157 papers remained, which we analyzed to build a picture of the tendencies and problems present in this research area, highlighting the computer vision methods, staining techniques, evaluation metrics, and the availability of the used datasets and computer code. As a result, we identified that the most used methods in the analyzed works are deep learning-based (70 papers), while fewer works employ classic computer vision only (101 papers). The most recurrent metric used for classification and object detection was the accuracy (33 papers and 5 papers), while for segmentation it was the Dice Similarity Coefficient (38 papers). Regarding staining techniques, Papanicolaou was the most employed one (130 papers), followed by H&E (20 papers) and Feulgen (5 papers). Twelve of the datasets used in the papers are publicly available, with the DTU/Herlev dataset being the most used one. We conclude that there still is a lack of high-quality datasets for many types of stains and most of the works are not mature enough to be applied in a daily clinical diagnostic routine. We also identified a growing tendency towards adopting deep learning-based approaches as the methods of choice.
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Affiliation(s)
- André Victória Matias
- Department of Informatics and Statistics, Federal University of Santa Catarina, Florianópolis, Brazil.
| | | | | | - Allan Cerentini
- Department of Informatics and Statistics, Federal University of Santa Catarina, Florianópolis, Brazil.
| | | | | | - Felipe Perozzo Daltoé
- Department of Pathology, Federal University of Santa Catarina, Florianópolis, Brazil.
| | - Marcelo Ricardo Stemmer
- Automation and Systems Department, Federal University of Santa Catarina, Florianópolis, Brazil.
| | - Aldo von Wangenheim
- Brazilian Institute for Digital Convergence, Federal University of Santa Catarina, Florianópolis, Brazil.
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Chandran V, Sumithra MG, Karthick A, George T, Deivakani M, Elakkiya B, Subramaniam U, Manoharan S. Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5584004. [PMID: 33997017 PMCID: PMC8112909 DOI: 10.1155/2021/5584004] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 03/31/2021] [Accepted: 04/20/2021] [Indexed: 12/17/2022]
Abstract
Traditional screening of cervical cancer type classification majorly depends on the pathologist's experience, which also has less accuracy. Colposcopy is a critical component of cervical cancer prevention. In conjunction with precancer screening and treatment, colposcopy has played an essential role in lowering the incidence and mortality from cervical cancer over the last 50 years. However, due to the increase in workload, vision screening causes misdiagnosis and low diagnostic efficiency. Medical image processing using the convolutional neural network (CNN) model shows its superiority for the classification of cervical cancer type in the field of deep learning. This paper proposes two deep learning CNN architectures to detect cervical cancer using the colposcopy images; one is the VGG19 (TL) model, and the other is CYENET. In the CNN architecture, VGG19 is adopted as a transfer learning for the studies. A new model is developed and termed as the Colposcopy Ensemble Network (CYENET) to classify cervical cancers from colposcopy images automatically. The accuracy, specificity, and sensitivity are estimated for the developed model. The classification accuracy for VGG19 was 73.3%. Relatively satisfied results are obtained for VGG19 (TL). From the kappa score of the VGG19 model, we can interpret that it comes under the category of moderate classification. The experimental results show that the proposed CYENET exhibited high sensitivity, specificity, and kappa scores of 92.4%, 96.2%, and 88%, respectively. The classification accuracy of the CYENET model is improved as 92.3%, which is 19% higher than the VGG19 (TL) model.
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Affiliation(s)
- Venkatesan Chandran
- Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Avinashi road, Coimbatore, 641407 Tamilnadu, India
| | - M. G. Sumithra
- Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Avinashi road, Coimbatore, 641407 Tamilnadu, India
| | - Alagar Karthick
- Renewable Energy Lab, Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Avinashi road, Coimbatore, 641407 Tamilnadu, India
| | - Tony George
- Department of Electrical and Electronics Engineering, Adi Shankara Institute of Engineering and Technology Mattoor, Kalady, Kerala 683574, India
| | - M. Deivakani
- Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Dindigul, 624622 Tamilnadu, India
| | - Balan Elakkiya
- Department of Electronics and Communication Engineering, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Tamilnadu 600062, India
| | - Umashankar Subramaniam
- Department of Communications and Networks, Renewable Energy Lab, College of Engineering, Prince, Sultan University, Riyadh 12435, Saudi Arabia
| | - S. Manoharan
- Department of Computer Science, School of Informatics and Electrical Engineering, Institute of Technology, Ambo University, Ambo, Post Box No. 19, Ethiopia
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A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11094091] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Prevention of cervical cancer could be performed using Pap smear image analysis. This test screens pre-neoplastic changes in the cervical epithelial cells; accurate screening can reduce deaths caused by the disease. Pap smear test analysis is exhaustive and repetitive work performed visually by a cytopathologist. This article proposes a workload-reducing algorithm for cervical cancer detection based on analysis of cell nuclei features within Pap smear images. We investigate eight traditional machine learning methods to perform a hierarchical classification. We propose a hierarchical classification methodology for computer-aided screening of cell lesions, which can recommend fields of view from the microscopy image based on the nuclei detection of cervical cells. We evaluate the performance of several algorithms against the Herlev and CRIC databases, using a varying number of classes during image classification. Results indicate that the hierarchical classification performed best when using Random Forest as the key classifier, particularly when compared with decision trees, k-NN, and the Ridge methods.
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Liu S, Yuan Z, Qiao X, Liu Q, Song K, Kong B, Su X. Light scattering pattern specific convolutional network static cytometry for label-free classification of cervical cells. Cytometry A 2021; 99:610-621. [PMID: 33840152 DOI: 10.1002/cyto.a.24349] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 03/11/2021] [Accepted: 04/01/2021] [Indexed: 12/12/2022]
Abstract
Cervical cancer is a major gynecological malignant tumor that threatens women's health. Current cytological methods have certain limitations for cervical cancer early screening. Light scattering patterns can reflect small differences in the internal structure of cells. In this study, we develop a light scattering pattern specific convolutional network (LSPS-net) based on deep learning algorithm and integrate it into a 2D light scattering static cytometry for automatic, label-free analysis of single cervical cells. An accuracy rate of 95.46% for the classification of normal cervical cells and cancerous ones (mixed C-33A and CaSki cells) is obtained. When applied for the subtyping of label-free cervical cell lines, we obtain an accuracy rate of 93.31% with our LSPS-net cytometric technique. Furthermore, the three-way classification of the above different types of cells has an overall accuracy rate of 90.90%, and comparisons with other feature descriptors and classification algorithms show the superiority of deep learning for automatic feature extraction. The LSPS-net static cytometry may potentially be used for cervical cancer early screening, which is rapid, automatic and label-free.
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Affiliation(s)
- Shanshan Liu
- School of Microelectronics, Shandong University, Jinan, China.,Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Zeng Yuan
- Department of obstetrics and gynecology, Qilu Hospital, Shandong University, Jinan, China
| | - Xu Qiao
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Qiao Liu
- Department of Molecular Medicine and Genetics, School of Basic Medicine Sciences, Shandong University, Jinan, China
| | - Kun Song
- Department of obstetrics and gynecology, Qilu Hospital, Shandong University, Jinan, China
| | - Beihua Kong
- Department of obstetrics and gynecology, Qilu Hospital, Shandong University, Jinan, China
| | - Xuantao Su
- School of Microelectronics, Shandong University, Jinan, China
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Holmström O, Linder N, Kaingu H, Mbuuko N, Mbete J, Kinyua F, Törnquist S, Muinde M, Krogerus L, Lundin M, Diwan V, Lundin J. Point-of-Care Digital Cytology With Artificial Intelligence for Cervical Cancer Screening in a Resource-Limited Setting. JAMA Netw Open 2021; 4:e211740. [PMID: 33729503 PMCID: PMC7970338 DOI: 10.1001/jamanetworkopen.2021.1740] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
IMPORTANCE Cervical cancer is highly preventable but remains a common and deadly cancer in areas without screening programs. The creation of a diagnostic system to digitize Papanicolaou test samples and analyze them using a cloud-based deep learning system (DLS) may provide needed cervical cancer screening to resource-limited areas. OBJECTIVE To determine whether artificial intelligence-supported digital microscopy diagnostics can be implemented in a resource-limited setting and used for analysis of Papanicolaou tests. DESIGN, SETTING, AND PARTICIPANTS In this diagnostic study, cervical smears from 740 HIV-positive women aged between 18 and 64 years were collected between September 1, 2018, and September 30, 2019. The smears were digitized with a portable slide scanner, uploaded to a cloud server using mobile networks, and used to train and validate a DLS for the detection of atypical cervical cells. This single-center study was conducted at a local health care center in rural Kenya. EXPOSURES Detection of squamous cell atypia in the digital samples by analysis with the DLS. MAIN OUTCOMES AND MEASURES The accuracy of the DLS in the detection of low- and high-grade squamous intraepithelial lesions in Papanicolaou test whole-slide images. RESULTS Papanicolaou test results from 740 HIV-positive women (mean [SD] age, 41.8 [10.3] years) were collected. The DLS was trained using 350 whole-slide images and validated on 361 whole-slide images (average size, 100 387 × 47 560 pixels). For detection of cervical cellular atypia, sensitivities were 95.7% (95% CI, 85.5%-99.5%) and 100% (95% CI, 82.4%-100%), and specificities were 84.7% (95% CI, 80.2%-88.5%) and 78.4% (95% CI, 73.6%-82.4%), compared with the pathologist assessment of digital and physical slides, respectively. Areas under the receiver operating characteristic curve were 0.94 and 0.96, respectively. Negative predictive values were high (99%-100%), and accuracy was high, particularly for the detection of high-grade lesions. Interrater agreement was substantial compared with the pathologist assessment of digital slides (κ = 0.72) and fair compared with the assessment of glass slides (κ = 0.36). No samples that were classified as high grade by manual sample analysis had false-negative assessments by the DLS. CONCLUSIONS AND RELEVANCE In this study, digital microscopy with artificial intelligence was implemented at a rural clinic and used to detect atypical cervical smears with a high sensitivity compared with visual sample analysis.
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Affiliation(s)
- Oscar Holmström
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Nina Linder
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Department of Women's and Children’s Health, International Maternal and Child Health, Uppsala University, Uppsala, Sweden
| | | | - Ngali Mbuuko
- Kinondo Kwetu Health Services Clinic, Kinondo, Kenya
| | - Jumaa Mbete
- Kinondo Kwetu Health Services Clinic, Kinondo, Kenya
| | - Felix Kinyua
- Kinondo Kwetu Health Services Clinic, Kinondo, Kenya
| | - Sara Törnquist
- Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
| | - Martin Muinde
- Kinondo Kwetu Health Services Clinic, Kinondo, Kenya
| | - Leena Krogerus
- Helsinki University Central Hospital Laboratory (HUSLAB), HUS Diagnostic Center, Helsinki and Uusimaa Hospital District, Helsinki, Finland
| | - Mikael Lundin
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Vinod Diwan
- Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
| | - Johan Lundin
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
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Cervical screening in high-income countries: the need for quality assurance, adjunct biomarkers and rational adaptation to HPV vaccination. Prev Med 2021; 144:106382. [PMID: 33359012 DOI: 10.1016/j.ypmed.2020.106382] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 12/17/2020] [Accepted: 12/18/2020] [Indexed: 02/07/2023]
Abstract
We here discuss human papillomavirus (HPV)-based screening avenues to achieve elimination of cervical cancer as a public health problem in high-income country (HIC) settings, covering both the most recent data on the performance of HPV testing, as well as the currently most robust triage methods that are known. We also provide an outlook to several other promising, yet not fully established, options for triage that have been proposed, including methylation, dual staining, machine learning, and artificial intelligence. Finally, we discuss the key issue of how to adapt screening in the presence of programmatic HPV vaccination, and how this combination can best be leveraged for comprehensive cancer control. We conclude that, for the HIC setting, evidence-based and effective cervical screening methods are readily available, but whichever method or platform is chosen, we would propose that recurring audits of performance and population attendance remain common denominators for maintaining successful disease prevention.
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