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Hosseini MS, Bejnordi BE, Trinh VQH, Chan L, Hasan D, Li X, Yang S, Kim T, Zhang H, Wu T, Chinniah K, Maghsoudlou S, Zhang R, Zhu J, Khaki S, Buin A, Chaji F, Salehi A, Nguyen BN, Samaras D, Plataniotis KN. Computational pathology: A survey review and the way forward. J Pathol Inform 2024; 15:100357. [PMID: 38420608 PMCID: PMC10900832 DOI: 10.1016/j.jpi.2023.100357] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 12/21/2023] [Accepted: 12/23/2023] [Indexed: 03/02/2024] Open
Abstract
Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.
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Affiliation(s)
- Mahdi S Hosseini
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | | | - Vincent Quoc-Huy Trinh
- Institute for Research in Immunology and Cancer of the University of Montreal, Montreal, QC H3T 1J4, Canada
| | - Lyndon Chan
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Danial Hasan
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Xingwen Li
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Stephen Yang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Taehyo Kim
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Haochen Zhang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Theodore Wu
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Kajanan Chinniah
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Sina Maghsoudlou
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | - Ryan Zhang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Jiadai Zhu
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Samir Khaki
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Andrei Buin
- Huron Digitial Pathology, St. Jacobs, ON N0B 2N0, Canada
| | - Fatemeh Chaji
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | - Ala Salehi
- Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
| | - Bich Ngoc Nguyen
- University of Montreal Hospital Center, Montreal, QC H2X 0C2, Canada
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, United States
| | - Konstantinos N Plataniotis
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
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2
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Zhang M, Cheng Q, Wei Z, Xu J, Wu S, Xu N, Zhao C, Yu L, Feng W. BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire. Brief Bioinform 2024; 25:bbae420. [PMID: 39177262 PMCID: PMC11342255 DOI: 10.1093/bib/bbae420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 07/24/2024] [Accepted: 08/08/2024] [Indexed: 08/24/2024] Open
Abstract
The T cell receptor (TCR) repertoire is pivotal to the human immune system, and understanding its nuances can significantly enhance our ability to forecast cancer-related immune responses. However, existing methods often overlook the intra- and inter-sequence interactions of T cell receptors (TCRs), limiting the development of sequence-based cancer-related immune status predictions. To address this challenge, we propose BertTCR, an innovative deep learning framework designed to predict cancer-related immune status using TCRs. BertTCR combines a pre-trained protein large language model with deep learning architectures, enabling it to extract deeper contextual information from TCRs. Compared to three state-of-the-art sequence-based methods, BertTCR improves the AUC on an external validation set for thyroid cancer detection by 21 percentage points. Additionally, this model was trained on over 2000 publicly available TCR libraries covering 17 types of cancer and healthy samples, and it has been validated on multiple public external datasets for its ability to distinguish cancer patients from healthy individuals. Furthermore, BertTCR can accurately classify various cancer types and healthy individuals. Overall, BertTCR is the advancing method for cancer-related immune status forecasting based on TCRs, offering promising potential for a wide range of immune status prediction tasks.
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Affiliation(s)
- Min Zhang
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, China
| | - Qi Cheng
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, China
| | - Zhenyu Wei
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, China
| | - Jiayu Xu
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, China
| | - Shiwei Wu
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, China
| | - Nan Xu
- Institute of Biomedical Engineering and Technology, Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, No. 500 Dongchuan Road, Shanghai, 200241, China
- Shanghai Unicar-Therapy Bio-medicine Technology Co., Ltd, No. 1525 Minqiang Road, Shanghai, 201612, China
| | - Chengkui Zhao
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, China
- Shanghai Unicar-Therapy Bio-medicine Technology Co., Ltd, No. 1525 Minqiang Road, Shanghai, 201612, China
| | - Lei Yu
- Institute of Biomedical Engineering and Technology, Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, No. 500 Dongchuan Road, Shanghai, 200241, China
- Shanghai Unicar-Therapy Bio-medicine Technology Co., Ltd, No. 1525 Minqiang Road, Shanghai, 201612, China
| | - Weixing Feng
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, China
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Yang X, Ding B, Qin J, Guo L, Zhao J, He Y. HVS-Unsup: Unsupervised cervical cell instance segmentation method based on human visual simulation. Comput Biol Med 2024; 171:108147. [PMID: 38387385 DOI: 10.1016/j.compbiomed.2024.108147] [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: 08/30/2023] [Revised: 01/22/2024] [Accepted: 02/12/2024] [Indexed: 02/24/2024]
Abstract
Instance segmentation plays an important role in the automatic diagnosis of cervical cancer. Although deep learning-based instance segmentation methods can achieve outstanding performance, they need large amounts of labeled data. This results in a huge consumption of manpower and material resources. To solve this problem, we propose an unsupervised cervical cell instance segmentation method based on human visual simulation, named HVS-Unsup. Our method simulates the process of human cell recognition and incorporates prior knowledge of cervical cells. Specifically, firstly, we utilize prior knowledge to generate three types of pseudo labels for cervical cells. In this way, the unsupervised instance segmentation is transformed to a supervised task. Secondly, we design a Nucleus Enhanced Module (NEM) and a Mask-Assisted Segmentation module (MAS) to address problems of cell overlapping, adhesion, and even scenarios involving visually indistinguishable cases. NEM can accurately locate the nuclei by the nuclei attention feature maps generated by point-level pseudo labels, and MAS can reduce the interference from impurities by updating the weight of the shallow network through the dice loss. Next, we propose a Category-Wise droploss (CW-droploss) to reduce cell omissions in lower-contrast images. Finally, we employ an iterative self-training strategy to rectify mislabeled instances. Experimental results on our dataset MS-cellSeg, the public datasets Cx22 and ISBI2015 demonstrate that HVS-Unsup outperforms existing mainstream unsupervised cervical cell segmentation methods.
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Affiliation(s)
- Xiaona Yang
- Harbin University of Science and Technology, School of Computer Science and Technology, Harbin, 150080, China
| | - Bo Ding
- Harbin University of Science and Technology, School of Computer Science and Technology, Harbin, 150080, China
| | - Jian Qin
- Harbin University of Science and Technology, School of Computer Science and Technology, Harbin, 150080, China
| | - Luyao Guo
- Harbin University of Science and Technology, School of Computer Science and Technology, Harbin, 150080, China
| | - Jing Zhao
- Northeast Forestry University, School of Mechanical and Electrical Engineering, Harbin, 150040, China
| | - Yongjun He
- Harbin Institute of Technology, School of Computer Science and Technology, Harbin, 150001, China.
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4
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Khan U, Afrakhteh S, Mento F, Mert G, Smargiassi A, Inchingolo R, Tursi F, Macioce VN, Perrone T, Iacca G, Demi L. Low-complexity lung ultrasound video scoring by means of intensity projection-based video compression. Comput Biol Med 2024; 169:107885. [PMID: 38141447 DOI: 10.1016/j.compbiomed.2023.107885] [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: 09/26/2023] [Revised: 11/27/2023] [Accepted: 12/18/2023] [Indexed: 12/25/2023]
Abstract
Since the outbreak of COVID-19, efforts have been made towards semi-quantitative analysis of lung ultrasound (LUS) data to assess the patient's condition. Several methods have been proposed in this regard, with a focus on frame-level analysis, which was then used to assess the condition at the video and prognostic levels. However, no extensive work has been done to analyze lung conditions directly at the video level. This study proposes a novel method for video-level scoring based on compression of LUS video data into a single image and automatic classification to assess patient's condition. The method utilizes maximum, mean, and minimum intensity projection-based compression of LUS video data over time. This enables to preserve hyper- and hypo-echoic data regions, while compressing the video down to a maximum of three images. The resulting images are then classified using a convolutional neural network (CNN). Finally, the worst predicted score given among the images is assigned to the corresponding video. The results show that this compression technique can achieve a promising agreement at the prognostic level (81.62%), while the video-level agreement remains comparable with the state-of-the-art (46.19%). Conclusively, the suggested method lays down the foundation for LUS video compression, shifting from frame-level to direct video-level analysis of LUS data.
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Affiliation(s)
- Umair Khan
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Sajjad Afrakhteh
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Federico Mento
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Gizem Mert
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Andrea Smargiassi
- Pulmonary Medicine Unit, Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Riccardo Inchingolo
- Pulmonary Medicine Unit, Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | | | | | - Tiziano Perrone
- Dipartimento di Emergenza ed Urgenza, Humanitas Gavazzeni Bergamo, Bergamo, Italy
| | - Giovanni Iacca
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Libertario Demi
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy.
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5
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Kaur M, Singh D, Kumar V, Lee HN. MLNet: Metaheuristics-Based Lightweight Deep Learning Network for Cervical Cancer Diagnosis. IEEE J Biomed Health Inform 2023; 27:5004-5014. [PMID: 36399582 DOI: 10.1109/jbhi.2022.3223127] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/06/2023]
Abstract
One of the leading causes of cancer-related deaths among women is cervical cancer. Early diagnosis and treatment can minimize the complications of this cancer. Recently, researchers have designed and implemented many deep learning-based automated cervical cancer diagnosis models. However, the majority of these models suffer from over-fitting, parameter tuning, and gradient vanishing problems. To overcome these problems, in this paper a metaheuristics-based lightweight deep learning network (MLNet) is proposed. Initially, the hyper-parameters tuning problem of convolutional neural network (CNN) is defined as a multi-objective problem. Particle swarm optimization (PSO) is used to optimally define the CNN architecture. Thereafter, Dynamically hybrid niching differential evolution (DHDE) is utilized to optimize the hyper-parameters of CNN layers. Each particle of PSO and solution of DHDE together represent the possible CNN configuration. F-score is used as a fitness function. The proposed MLNet is trained and validated on three benchmark cervical cancer datasets. On the Herlev dataset, MLNet outperforms the existing models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 1.6254%, 1.5178%, 1.5780%, 1.7145%, and 1.4890%, respectively. Also, on the SIPaKMeD dataset, MLNet achieves better performance than the existing models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 2.1250%, 2.2455%, 1.9074%, 1.9258%, and 1.8975%, respectively. Finally, on the Mendeley LBC dataset, MLNet achieves better performance than the competitive models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 1.4680%, 1.5845%, 1.3582%, 1.3926%, and 1.4125%, respectively.
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6
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Kumar M, Patel AK, Biswas M, Shitharth S. Attention-based bidirectional-long short-term memory for abnormal human activity detection. Sci Rep 2023; 13:14442. [PMID: 37660111 PMCID: PMC10475069 DOI: 10.1038/s41598-023-41231-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 08/23/2023] [Indexed: 09/04/2023] Open
Abstract
Abnormal human behavior must be monitored and controlled in today's technology-driven era, since it may cause damage to society in the form of assault or web-based violence, such as direct harm to a person or the propagation of hate crimes through the internet. Several authors have attempted to address this issue, but no one has yet come up with a solution that is both practical and workable. Recently, deep learning models have become popular as a means of handling massive amounts of data but their potential to categorize the aberrant human activity remains unexplored. Using a convolutional neural network (CNN), a bidirectional long short-term memory (Bi-LSTM), and an attention mechanism to pay attention to the unique spatiotemporal characteristics of raw video streams, a deep-learning approach has been implemented in the proposed framework to detect anomalous human activity. After analyzing the video, our suggested architecture can reliably assign an abnormal human behavior to its designated category. Analytic findings comparing the suggested architecture to state-of-the-art algorithms reveal an accuracy of 98.9%, 96.04%, and 61.04% using the UCF11, UCF50, and subUCF crime datasets, respectively.
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Affiliation(s)
- Manoj Kumar
- JSS Academy of Technical Education, Noida, India
- National Institute of Technology Kurukshetra, Kurukshetra, India
| | | | - Mantosh Biswas
- National Institute of Technology Kurukshetra, Kurukshetra, India
| | - S Shitharth
- Kebri Dehar University, KebriDehar, Ethiopia.
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7
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Su R, He H, Sun C, Wang X, Liu X. Prediction of drug-induced hepatotoxicity based on histopathological whole slide images. Methods 2023; 212:31-38. [PMID: 36706825 DOI: 10.1016/j.ymeth.2023.01.005] [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: 12/12/2022] [Revised: 12/30/2022] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
Liver is an important metabolic organ in human body and is sensitive to toxic chemicals or drugs. Adverse reactions caused by drug hepatotoxicity will damage the liver and hepatotoxicity is the leading cause of removal of approved drugs from the market. Therefore, it is of great significance to identify liver toxicity as early as possible in the drug development process. In this study, we developed a predictive model for drug hepatotoxicity based on histopathological whole slide images (WSI) which are the by-product of drug experiments and have received little attention. To better represent the WSIs, we constructed a graph representation for each WSI by dividing it into small patches, taking sampled patches as nodes and calculating the correlation coefficients between node features as the edges of the graph structure. Then a WSI-level graph convolutional network (GCN) was built to effectively extract the node information of the graph and predict the toxicity. In addition, we introduced a gated attention global context vector (gaGCV) to combine the global context to make node features to contain more comprehensive information. The results validated on rat liver in vivo data from the Open TG-GATES show that the use of WSI for the prediction of toxicity is feasible and effective.
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Affiliation(s)
- Ran Su
- School of Computer Software, College of Intelligence and Computing, Tianjin University, China
| | - Hao He
- School of Computer Software, College of Intelligence and Computing, Tianjin University, China
| | | | - Xiaomin Wang
- National Clinical Research Center for Infectious Diseases, Shenzhen, Guangdong, China.
| | - Xiaofeng Liu
- Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
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8
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Rauf Z, Sohail A, Khan SH, Khan A, Gwak J, Maqbool M. Attention-guided multi-scale deep object detection framework for lymphocyte analysis in IHC histological images. Microscopy (Oxf) 2023; 72:27-42. [PMID: 36239597 DOI: 10.1093/jmicro/dfac051] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 09/21/2022] [Accepted: 10/13/2022] [Indexed: 11/14/2022] Open
Abstract
Tumor-infiltrating lymphocytes are specialized lymphocytes that can detect and kill cancerous cells. Their detection poses many challenges due to significant morphological variations, overlapping occurrence, artifact regions and high-class resemblance between clustered areas and artifacts. In this regard, a Lymphocyte Analysis Framework based on Deep Convolutional neural network (DC-Lym-AF) is proposed to analyze lymphocytes in immunohistochemistry images. The proposed framework comprises (i) pre-processing, (ii) screening phase, (iii) localization phase and (iv) post-processing. In the screening phase, a custom convolutional neural network architecture (lymphocyte dilated network) is developed to screen lymphocytic regions by performing a patch-level classification. This proposed architecture uses dilated convolutions and shortcut connections to capture multi-level variations and ensure reference-based learning. In contrast, the localization phase utilizes an attention-guided multi-scale lymphocyte detector to detect lymphocytes. The proposed detector extracts refined and multi-scale features by exploiting dilated convolutions, attention mechanism and feature pyramid network (FPN) using its custom attention-aware backbone. The proposed DC-Lym-AF shows exemplary performance on the NuClick dataset compared with the existing detection models, with an F-score and precision of 0.84 and 0.83, respectively. We verified the generalizability of our proposed framework by participating in a publically open LYON'19 challenge. Results in terms of detection rate (0.76) and F-score (0.73) suggest that the proposed DC-Lym-AF can effectively detect lymphocytes in immunohistochemistry-stained images collected from different laboratories. In addition, its promising generalization on several datasets implies that it can be turned into a medical diagnostic tool to investigate various histopathological problems. Graphical Abstract.
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Affiliation(s)
- Zunaira Rauf
- Pattern Recognition Lab, Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad 45650, Pakistan.,PIEAS Artificial Intelligence Center, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad 45650, Pakistan
| | - Anabia Sohail
- Pattern Recognition Lab, Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad 45650, Pakistan.,Department of Computer Science, Faculty of Computing and Artificial Intelligence, Air University, E-9, Islamabad 44230, Pakistan
| | - Saddam Hussain Khan
- Pattern Recognition Lab, Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad 45650, Pakistan.,Department of Computer Systems Engineering, University of Engineering and Applied Sciences, Swat, Khyber Pakhtunkhwa 19130, Pakistan
| | - Asifullah Khan
- Pattern Recognition Lab, Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad 45650, Pakistan.,PIEAS Artificial Intelligence Center, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad 45650, Pakistan.,Center for Mathematical Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad 45650, Pakistan
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju 27469, Republic of Korea
| | - Muhammad Maqbool
- The University of Alabama at Birmingham, 1720 2nd Ave South, Birmingham, AL 35294, USA
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9
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Kakotkin VV, Semina EV, Zadorkina TG, Agapov MA. Prevention Strategies and Early Diagnosis of Cervical Cancer: Current State and Prospects. Diagnostics (Basel) 2023; 13:diagnostics13040610. [PMID: 36832098 PMCID: PMC9955852 DOI: 10.3390/diagnostics13040610] [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] [Received: 12/14/2022] [Revised: 02/03/2023] [Accepted: 02/05/2023] [Indexed: 02/11/2023] Open
Abstract
Cervical cancer ranks third among all new cancer cases and causes of cancer deaths in females. The paper provides an overview of cervical cancer prevention strategies employed in different regions, with incidence and mortality rates ranging from high to low. It assesses the effectiveness of approaches proposed by national healthcare systems by analysing data published in the National Library of Medicine (Pubmed) since 2018 featuring the following keywords: "cervical cancer prevention", "cervical cancer screening", "barriers to cervical cancer prevention", "premalignant cervical lesions" and "current strategies". WHO's 90-70-90 global strategy for cervical cancer prevention and early screening has proven effective in different countries in both mathematical models and clinical practice. The data analysis carried out within this study identified promising approaches to cervical cancer screening and prevention, which can further enhance the effectiveness of the existing WHO strategy and national healthcare systems. One such approach is the application of AI technologies for detecting precancerous cervical lesions and choosing treatment strategies. As such studies show, the use of AI can not only increase detection accuracy but also ease the burden on primary care.
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Affiliation(s)
- Viktor V. Kakotkin
- Scientific and Educational Cluster MEDBIO, Immanuel Kant Baltic Federal University, A. Nevskogo St., 14, 236041 Kaliningrad, Russia
| | - Ekaterina V. Semina
- Scientific and Educational Cluster MEDBIO, Immanuel Kant Baltic Federal University, A. Nevskogo St., 14, 236041 Kaliningrad, Russia
| | - Tatiana G. Zadorkina
- Kaliningrad Regional Centre for Specialised Medical Care, Barnaulskaia Street, 6, 236006 Kaliningrad, Russia
| | - Mikhail A. Agapov
- Scientific and Educational Cluster MEDBIO, Immanuel Kant Baltic Federal University, A. Nevskogo St., 14, 236041 Kaliningrad, Russia
- Correspondence: ; Tel.: +7-(4012)-59-55-95
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10
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Rajaraman S, Zamzmi G, Yang F, Xue Z, Antani SK. Data Characterization for Reliable AI in Medicine. RECENT TRENDS IN IMAGE PROCESSING AND PATTERN RECOGNITION : 5TH INTERNATIONAL CONFERENCE, RTIP2R 2022, KINGSVILLE, TX, USA, DECEMBER 01-02, 2022, REVISED SELECTED PAPERS. INTERNATIONAL CONFERENCE ON RECENT TRENDS IN IMAGE PROCESSING AND... 2023; 1704:3-11. [PMID: 36780238 PMCID: PMC9912175 DOI: 10.1007/978-3-031-23599-3_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Research in Artificial Intelligence (AI)-based medical computer vision algorithms bear promises to improve disease screening, diagnosis, and subsequently patient care. However, these algorithms are highly impacted by the characteristics of the underlying data. In this work, we discuss various data characteristics, namely Volume, Veracity, Validity, Variety, and Velocity, that impact the design, reliability, and evolution of machine learning in medical computer vision. Further, we discuss each characteristic and the recent works conducted in our research lab that informed our understanding of the impact of these characteristics on the design of medical decision-making algorithms and outcome reliability.
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Affiliation(s)
- Sivaramakrishnan Rajaraman
- Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda MD 20894, USA
| | - Ghada Zamzmi
- Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda MD 20894, USA
| | - Feng Yang
- Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda MD 20894, USA
| | - Zhiyun Xue
- Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda MD 20894, USA
| | - Sameer K Antani
- Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda MD 20894, USA
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11
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Allahqoli L, Laganà AS, Mazidimoradi A, Salehiniya H, Günther V, Chiantera V, Karimi Goghari S, Ghiasvand MM, Rahmani A, Momenimovahed Z, Alkatout I. Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review. Diagnostics (Basel) 2022; 12:2771. [PMID: 36428831 PMCID: PMC9689914 DOI: 10.3390/diagnostics12112771] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/06/2022] [Accepted: 11/10/2022] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVE The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions. MATERIALS AND METHODS Comprehensive searches were performed on three databases: Medline, Web of Science Core Collection (Indexes = SCI-EXPANDED, SSCI, A & HCI Timespan) and Scopus to find papers published until July 2022. Articles that applied any AI technique for the prediction, screening, and diagnosis of cervical cancer were included in the review. No time restriction was applied. Articles were searched, screened, incorporated, and analyzed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. RESULTS The primary search yielded 2538 articles. After screening and evaluation of eligibility, 117 studies were incorporated in the review. AI techniques were found to play a significant role in screening systems for pre-cancerous and cancerous cervical lesions. The accuracy of the algorithms in predicting cervical cancer varied from 70% to 100%. AI techniques make a distinction between cancerous and normal Pap smears with 80-100% accuracy. AI is expected to serve as a practical tool for doctors in making accurate clinical diagnoses. The reported sensitivity and specificity of AI in colposcopy for the detection of CIN2+ were 71.9-98.22% and 51.8-96.2%, respectively. CONCLUSION The present review highlights the acceptable performance of AI systems in the prediction, screening, or detection of cervical cancer and pre-cancerous lesions, especially when faced with a paucity of specialized centers or medical resources. In combination with human evaluation, AI could serve as a helpful tool in the interpretation of cervical smears or images.
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Affiliation(s)
- Leila Allahqoli
- Midwifery Department, Ministry of Health and Medical Education, Tehran 1467664961, Iran
| | - Antonio Simone Laganà
- Unit of Gynecologic Oncology, ARNAS “Civico-Di Cristina-Benfratelli”, Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (PROMISE), University of Palermo, 90127 Palermo, Italy
| | - Afrooz Mazidimoradi
- Neyriz Public Health Clinic, Shiraz University of Medical Sciences, Shiraz 7134814336, Iran
| | - Hamid Salehiniya
- Social Determinants of Health Research Center, Birjand University of Medical Sciences, Birjand 9717853577, Iran
| | - Veronika Günther
- University Hospitals Schleswig-Holstein, Campus Kiel, Kiel School of Gynaecological Endoscopy, Arnold-Heller-Str. 3, Haus 24, 24105 Kiel, Germany
| | - Vito Chiantera
- Unit of Gynecologic Oncology, ARNAS “Civico-Di Cristina-Benfratelli”, Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (PROMISE), University of Palermo, 90127 Palermo, Italy
| | - Shirin Karimi Goghari
- School of Industrial and Systems Engineering, Tarbiat Modares University (TMU), Tehran 1411713114, Iran
| | - Mohammad Matin Ghiasvand
- Department of Computer Engineering, Amirkabir University of Technology (AUT), Tehran 1591634311, Iran
| | - Azam Rahmani
- Nursing and Midwifery Care Research Centre, School of Nursing and Midwifery, Tehran University of Medical Sciences, Tehran 141973317, Iran
| | - Zohre Momenimovahed
- Reproductive Health Department, Qom University of Medical Sciences, Qom 3716993456, Iran
| | - Ibrahim Alkatout
- University Hospitals Schleswig-Holstein, Campus Kiel, Kiel School of Gynaecological Endoscopy, Arnold-Heller-Str. 3, Haus 24, 24105 Kiel, Germany
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12
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Yang Y, Wang N, Shi X, Wang Y, Yang C, Fan J, Jia X. Construction of an immune infiltration landscape based on immune-related genes in cervical cancer. Comput Biol Med 2022; 146:105638. [DOI: 10.1016/j.compbiomed.2022.105638] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 04/29/2022] [Accepted: 04/30/2022] [Indexed: 12/14/2022]
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13
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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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