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Clement David-Olawade A, Olawade DB, Vanderbloemen L, Rotifa OB, Fidelis SC, Egbon E, Akpan AO, Adeleke S, Ghose A, Boussios S. AI-Driven Advances in Low-Dose Imaging and Enhancement-A Review. Diagnostics (Basel) 2025; 15:689. [PMID: 40150031 PMCID: PMC11941271 DOI: 10.3390/diagnostics15060689] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2025] [Revised: 02/24/2025] [Accepted: 03/05/2025] [Indexed: 03/29/2025] Open
Abstract
The widespread use of medical imaging techniques such as X-rays and computed tomography (CT) has raised significant concerns regarding ionizing radiation exposure, particularly among vulnerable populations requiring frequent imaging. Achieving a balance between high-quality diagnostic imaging and minimizing radiation exposure remains a fundamental challenge in radiology. Artificial intelligence (AI) has emerged as a transformative solution, enabling low-dose imaging protocols that enhance image quality while significantly reducing radiation doses. This review explores the role of AI-assisted low-dose imaging, particularly in CT, X-ray, and magnetic resonance imaging (MRI), highlighting advancements in deep learning models, convolutional neural networks (CNNs), and other AI-based approaches. These technologies have demonstrated substantial improvements in noise reduction, artifact removal, and real-time optimization of imaging parameters, thereby enhancing diagnostic accuracy while mitigating radiation risks. Additionally, AI has contributed to improved radiology workflow efficiency and cost reduction by minimizing the need for repeat scans. The review also discusses emerging directions in AI-driven medical imaging, including hybrid AI systems that integrate post-processing with real-time data acquisition, personalized imaging protocols tailored to patient characteristics, and the expansion of AI applications to fluoroscopy and positron emission tomography (PET). However, challenges such as model generalizability, regulatory constraints, ethical considerations, and computational requirements must be addressed to facilitate broader clinical adoption. AI-driven low-dose imaging has the potential to revolutionize radiology by enhancing patient safety, optimizing imaging quality, and improving healthcare efficiency, paving the way for a more advanced and sustainable future in medical imaging.
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Affiliation(s)
| | - David B. Olawade
- Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London E16 2RD, UK
- Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK;
- Department of Public Health, York St. John University, London E14 2BA, UK
| | - Laura Vanderbloemen
- Department of Primary Care and Public Health, Imperial College London, London SW7 2AZ, UK;
- School of Health, Sport and Bioscience, University of East London, London E16 2RD, UK
| | - Oluwayomi B. Rotifa
- Department of Radiology, Afe Babalola University MultiSystem Hospital, Ado-Ekiti 360102, Ekiti State, Nigeria;
| | - Sandra Chinaza Fidelis
- School of Nursing and Midwifery, University of Central Lancashire, Preston Campus, Preston PR1 2HE, UK;
| | - Eghosasere Egbon
- Department of Tissue Engineering and Regenerative Medicine, Faculty of Life Science Engineering, FH Technikum, 1200 Vienna, Austria;
| | | | - Sola Adeleke
- Guy’s Cancer Centre, Guy’s and St. Thomas’ NHS Foundation Trust, London SE1 9RT, UK;
- School of Cancer & Pharmaceutical Sciences, King’s College London, Strand, London WC2R 2LS, UK
| | - Aruni Ghose
- Department of Medical Oncology, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK;
- United Kingdom and Ireland Global Cancer Network, Manchester M20 4BX, UK
| | - Stergios Boussios
- Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK;
- School of Cancer & Pharmaceutical Sciences, King’s College London, Strand, London WC2R 2LS, UK
- Department of Medical Oncology, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK;
- Faculty of Medicine, Health, and Social Care, Canterbury Christ Church University, Canterbury CT1 1QU, UK
- Kent Medway Medical School, University of Kent, Canterbury CT2 7NZ, UK
- AELIA Organization, 57001 Thessaloniki, Greece
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Radhabai PR, Kvn K, Shanmugam A, Imoize AL. An effective no-reference image quality index prediction with a hybrid Artificial Intelligence approach for denoised MRI images. BMC Med Imaging 2024; 24:208. [PMID: 39134983 PMCID: PMC11318287 DOI: 10.1186/s12880-024-01387-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2024] [Accepted: 08/01/2024] [Indexed: 08/16/2024] Open
Abstract
As the quantity and significance of digital pictures in the medical industry continue to increase, Image Quality Assessment (IQA) has recently become a prevalent subject in the research community. Due to the wide range of distortions that Magnetic Resonance Images (MRI) can experience and the wide variety of information they contain, No-Reference Image Quality Assessment (NR-IQA) has always been a challenging study issue. In an attempt to address this issue, a novel hybrid Artificial Intelligence (AI) is proposed to analyze NR-IQ in massive MRI data. First, the features from the denoised MRI images are extracted using the gray level run length matrix (GLRLM) and EfficientNet B7 algorithm. Next, the Multi-Objective Reptile Search Algorithm (MRSA) was proposed for optimal feature vector selection. Then, the Self-evolving Deep Belief Fuzzy Neural network (SDBFN) algorithm was proposed for the effective NR-IQ analysis. The implementation of this research is executed using MATLAB software. The simulation results are compared with the various conventional methods in terms of correlation coefficient (PLCC), Root Mean Square Error (RMSE), Spearman Rank Order Correlation Coefficient (SROCC) and Kendall Rank Order Correlation Coefficient (KROCC), and Mean Absolute Error (MAE). In addition, our proposed approach yielded a quality number approximately we achieved significant 20% improvement than existing methods, with the PLCC parameter showing a notable increase compared to current techniques. Moreover, the RMSE number decreased by 12% when compared to existing methods. Graphical representations indicated mean MAE values of 0.02 for MRI knee dataset, 0.09 for MRI brain dataset, and 0.098 for MRI breast dataset, showcasing significantly lower MAE values compared to the baseline models.
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Affiliation(s)
| | - Kavitha Kvn
- Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Ashok Shanmugam
- Department of Electronics and Communication Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamil Nadu, India
| | - Agbotiname Lucky Imoize
- Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos, 100213, Nigeria.
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Piffer S, Ubaldi L, Tangaro S, Retico A, Talamonti C. Tackling the small data problem in medical image classification with artificial intelligence: a systematic review. PROGRESS IN BIOMEDICAL ENGINEERING (BRISTOL, ENGLAND) 2024; 6:032001. [PMID: 39655846 DOI: 10.1088/2516-1091/ad525b] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 05/30/2024] [Indexed: 12/18/2024]
Abstract
Though medical imaging has seen a growing interest in AI research, training models require a large amount of data. In this domain, there are limited sets of data available as collecting new data is either not feasible or requires burdensome resources. Researchers are facing with the problem of small datasets and have to apply tricks to fight overfitting. 147 peer-reviewed articles were retrieved from PubMed, published in English, up until 31 July 2022 and articles were assessed by two independent reviewers. We followed the Preferred Reporting Items for Systematic reviews and Meta-Analyse (PRISMA) guidelines for the paper selection and 77 studies were regarded as eligible for the scope of this review. Adherence to reporting standards was assessed by using TRIPOD statement (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis). To solve the small data issue transfer learning technique, basic data augmentation and generative adversarial network were applied in 75%, 69% and 14% of cases, respectively. More than 60% of the authors performed a binary classification given the data scarcity and the difficulty of the tasks. Concerning generalizability, only four studies explicitly stated an external validation of the developed model was carried out. Full access to all datasets and code was severely limited (unavailable in more than 80% of studies). Adherence to reporting standards was suboptimal (<50% adherence for 13 of 37 TRIPOD items). The goal of this review is to provide a comprehensive survey of recent advancements in dealing with small medical images samples size. Transparency and improve quality in publications as well as follow existing reporting standards are also supported.
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Affiliation(s)
- Stefano Piffer
- Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
- National Institute for Nuclear Physics (INFN), Florence Division, Florence, Italy
| | - Leonardo Ubaldi
- Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
- National Institute for Nuclear Physics (INFN), Florence Division, Florence, Italy
| | - Sabina Tangaro
- Department of Soil, Plant and Food Sciences, University of Bari Aldo Moro, Bari, Italy
- INFN, Bari Division, Bari, Italy
| | | | - Cinzia Talamonti
- Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
- National Institute for Nuclear Physics (INFN), Florence Division, Florence, Italy
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Loizillon S, Bottani S, Maire A, Ströer S, Dormont D, Colliot O, Burgos N. Automatic motion artefact detection in brain T1-weighted magnetic resonance images from a clinical data warehouse using synthetic data. Med Image Anal 2024; 93:103073. [PMID: 38176355 DOI: 10.1016/j.media.2023.103073] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 12/12/2023] [Accepted: 12/21/2023] [Indexed: 01/06/2024]
Abstract
Containing the medical data of millions of patients, clinical data warehouses (CDWs) represent a great opportunity to develop computational tools. Magnetic resonance images (MRIs) are particularly sensitive to patient movements during image acquisition, which will result in artefacts (blurring, ghosting and ringing) in the reconstructed image. As a result, a significant number of MRIs in CDWs are corrupted by these artefacts and may be unusable. Since their manual detection is impossible due to the large number of scans, it is necessary to develop tools to automatically exclude (or at least identify) images with motion in order to fully exploit CDWs. In this paper, we propose a novel transfer learning method from research to clinical data for the automatic detection of motion in 3D T1-weighted brain MRI. The method consists of two steps: a pre-training on research data using synthetic motion, followed by a fine-tuning step to generalise our pre-trained model to clinical data, relying on the labelling of 4045 images. The objectives were both (1) to be able to exclude images with severe motion, (2) to detect mild motion artefacts. Our approach achieved excellent accuracy for the first objective with a balanced accuracy nearly similar to that of the annotators (balanced accuracy>80 %). However, for the second objective, the performance was weaker and substantially lower than that of human raters. Overall, our framework will be useful to take advantage of CDWs in medical imaging and highlight the importance of a clinical validation of models trained on research data.
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Affiliation(s)
- Sophie Loizillon
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris 75013, France
| | - Simona Bottani
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris 75013, France
| | - Aurélien Maire
- AP-HP, Innovation & Données - Département des Services Numériques, Paris 75012, France
| | - Sebastian Ströer
- AP-HP, Hôpital de la Pitié Salpêtrière, Department of Neuroradiology, Paris 75013, France
| | - Didier Dormont
- AP-HP, Hôpital de la Pitié Salpêtrière, Department of Neuroradiology, Paris 75013, France; Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, DMU DIAMENT, Paris 75013, France
| | - Olivier Colliot
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris 75013, France
| | - Ninon Burgos
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris 75013, France.
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Hendriks J, Mutsaerts HJ, Joules R, Peña-Nogales Ó, Rodrigues PR, Wolz R, Burchell GL, Barkhof F, Schrantee A. A systematic review of (semi-)automatic quality control of T1-weighted MRI scans. Neuroradiology 2024; 66:31-42. [PMID: 38047983 PMCID: PMC10761394 DOI: 10.1007/s00234-023-03256-0] [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: 09/28/2023] [Accepted: 11/16/2023] [Indexed: 12/05/2023]
Abstract
PURPOSE Artifacts in magnetic resonance imaging (MRI) scans degrade image quality and thus negatively affect the outcome measures of clinical and research scanning. Considering the time-consuming and subjective nature of visual quality control (QC), multiple (semi-)automatic QC algorithms have been developed. This systematic review presents an overview of the available (semi-)automatic QC algorithms and software packages designed for raw, structural T1-weighted (T1w) MRI datasets. The objective of this review was to identify the differences among these algorithms in terms of their features of interest, performance, and benchmarks. METHODS We queried PubMed, EMBASE (Ovid), and Web of Science databases on the fifth of January 2023, and cross-checked reference lists of retrieved papers. Bias assessment was performed using PROBAST (Prediction model Risk Of Bias ASsessment Tool). RESULTS A total of 18 distinct algorithms were identified, demonstrating significant variations in methods, features, datasets, and benchmarks. The algorithms were categorized into rule-based, classical machine learning-based, and deep learning-based approaches. Numerous unique features were defined, which can be roughly divided into features capturing entropy, contrast, and normative measures. CONCLUSION Due to dataset-specific optimization, it is challenging to draw broad conclusions about comparative performance. Additionally, large variations exist in the used datasets and benchmarks, further hindering direct algorithm comparison. The findings emphasize the need for standardization and comparative studies for advancing QC in MR imaging. Efforts should focus on identifying a dataset-independent measure as well as algorithm-independent methods for assessing the relative performance of different approaches.
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Affiliation(s)
- Janine Hendriks
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, PK -1, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands.
| | - Henk-Jan Mutsaerts
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, PK -1, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
| | | | | | | | - Robin Wolz
- IXICO Plc, London, EC1A 9PN, UK
- Imperial College London, London, SW7 2BX, UK
| | - George L Burchell
- Medical Library, Vrije Universiteit Amsterdam, Amsterdam, 1081 HV, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, PK -1, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, WC1N 3BG, UK
| | - Anouk Schrantee
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location AMC, Amsterdam, 1105 AZ, The Netherlands
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Wang Z, Song Y, Zhao B, Zhong Z, Yao L, Lv F, Li B, Hu Y. A Soft-Reference Breast Ultrasound Image Quality Assessment Method That Considers the Local Lesion Area. Bioengineering (Basel) 2023; 10:940. [PMID: 37627825 PMCID: PMC10451797 DOI: 10.3390/bioengineering10080940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/23/2023] [Accepted: 08/03/2023] [Indexed: 08/27/2023] Open
Abstract
The quality of breast ultrasound images has a significant impact on the accuracy of disease diagnosis. Existing image quality assessment (IQA) methods usually use pixel-level feature statistical methods or end-to-end deep learning methods, which focus on the global image quality but ignore the image quality of the lesion region. However, in clinical practice, doctors' evaluation of ultrasound image quality relies more on the local area of the lesion, which determines the diagnostic value of ultrasound images. In this study, a global-local integrated IQA framework for breast ultrasound images was proposed to learn doctors' clinical evaluation standards. In this study, 1285 breast ultrasound images were collected and scored by experienced doctors. After being classified as either images with lesions or images without lesions, they were evaluated using soft-reference IQA or bilinear CNN IQA, respectively. Experiments showed that for ultrasound images with lesions, our proposed soft-reference IQA achieved PLCC 0.8418 with doctors' annotation, while the existing end-to-end deep learning method that did not consider the local lesion features only achieved PLCC 0.6606. Due to the accuracy improvement for the images with lesions, our proposed global-local integrated IQA framework had better performance in the IQA task than the existing end-to-end deep learning method, with PLCC improving from 0.8306 to 0.8851.
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Affiliation(s)
- Ziwen Wang
- School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China;
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (Y.S.); (L.Y.); (Y.H.)
| | - Yuxin Song
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (Y.S.); (L.Y.); (Y.H.)
| | - Baoliang Zhao
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (Y.S.); (L.Y.); (Y.H.)
| | - Zhaoming Zhong
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China (F.L.)
- Department of Ultrasound, The Third Medical Centre of Chinese PLA General Hospital, Beijing 100039, China
| | - Liang Yao
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (Y.S.); (L.Y.); (Y.H.)
| | - Faqin Lv
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China (F.L.)
- Department of Ultrasound, The Third Medical Centre of Chinese PLA General Hospital, Beijing 100039, China
| | - Bing Li
- School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China;
| | - Ying Hu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (Y.S.); (L.Y.); (Y.H.)
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Khan ZA, Beghdadi A, Kaaniche M, Alaya-Cheikh F, Gharbi O. A neural network based framework for effective laparoscopic video quality assessment. Comput Med Imaging Graph 2022; 101:102121. [PMID: 36174307 DOI: 10.1016/j.compmedimag.2022.102121] [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: 04/07/2022] [Revised: 08/22/2022] [Accepted: 08/30/2022] [Indexed: 01/27/2023]
Abstract
Video quality assessment is a challenging problem having a critical significance in the context of medical imaging. For instance, in laparoscopic surgery, the acquired video data suffers from different kinds of distortion that not only hinder surgery performance but also affect the execution of subsequent tasks in surgical navigation and robotic surgeries. For this reason, we propose in this paper neural network-based approaches for distortion classification as well as quality prediction. More precisely, a Residual Network (ResNet) based approach is firstly developed for simultaneous ranking and classification task. Then, this architecture is extended to make it appropriate for the quality prediction task by using an additional Fully Connected Neural Network (FCNN). To train the overall architecture (ResNet and FCNN models), transfer learning and end-to-end learning approaches are investigated. Experimental results, carried out on a new laparoscopic video quality database, have shown the efficiency of the proposed methods compared to recent conventional and deep learning based approaches.
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Affiliation(s)
- Zohaib Amjad Khan
- Université Sorbonne Paris Nord, L2TI, UR 3043, F-93430, Villetaneuse, France
| | - Azeddine Beghdadi
- Université Sorbonne Paris Nord, L2TI, UR 3043, F-93430, Villetaneuse, France.
| | - Mounir Kaaniche
- Université Sorbonne Paris Nord, L2TI, UR 3043, F-93430, Villetaneuse, France
| | | | - Osama Gharbi
- Université Sorbonne Paris Nord, L2TI, UR 3043, F-93430, Villetaneuse, France
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Lim A, Lo J, Wagner MW, Ertl-Wagner B, Sussman D. Automatic Artifact Detection Algorithm in Fetal MRI. Front Artif Intell 2022; 5:861791. [PMID: 35783351 PMCID: PMC9244144 DOI: 10.3389/frai.2022.861791] [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: 01/25/2022] [Accepted: 05/16/2022] [Indexed: 11/13/2022] Open
Abstract
Fetal MR imaging is subject to artifacts including motion, chemical shift, and radiofrequency artifacts. Currently, such artifacts are detected by the MRI operator, a process which is subjective, time consuming, and prone to errors. We propose a novel algorithm, RISE-Net, that can consistently, automatically, and objectively detect artifacts in 3D fetal MRI. It makes use of a CNN ensemble approach where the first CNN aims to identify and classify any artifacts in the image, and the second CNN uses regression to determine the severity of the detected artifacts. The main mechanism in RISE-Net is the stacked Residual, Inception, Squeeze and Excitation (RISE) blocks. This classification network achieved an accuracy of 90.34% and a F1 score of 90.39% and outperformed other state-of-the-art architectures, such as VGG-16, Inception, ResNet-50, ReNet-Inception, SE-ResNet, and SE-Inception. The severity regression network had an MSE of 0.083 across all classes. The presented algorithm facilitates rapid and accurate fetal MRI quality assurance that can be implemented into clinical use.
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Affiliation(s)
- Adam Lim
- Department of Electrical, Computer and Biomedical Engineering, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University, Toronto, ON, Canada
- Institute for Biomedical Engineering, Science and Technology (iBEST), Toronto Metropolitan University and St. Michael's Hospital, Toronto, ON, Canada
| | - Justin Lo
- Department of Electrical, Computer and Biomedical Engineering, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University, Toronto, ON, Canada
- Institute for Biomedical Engineering, Science and Technology (iBEST), Toronto Metropolitan University and St. Michael's Hospital, Toronto, ON, Canada
| | - Matthias W. Wagner
- Division of Neuroradiology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Birgit Ertl-Wagner
- Division of Neuroradiology, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Medical Imaging, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Dafna Sussman
- Department of Electrical, Computer and Biomedical Engineering, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University, Toronto, ON, Canada
- Institute for Biomedical Engineering, Science and Technology (iBEST), Toronto Metropolitan University and St. Michael's Hospital, Toronto, ON, Canada
- Department of Obstetrics and Gynecology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- *Correspondence: Dafna Sussman
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Intelligent Question Answering System by Deep Convolutional Neural Network in Finance and Economics Teaching. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5755327. [PMID: 35096043 PMCID: PMC8799335 DOI: 10.1155/2022/5755327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 12/28/2021] [Indexed: 11/17/2022]
Abstract
The question answering link in the traditional teaching method is analyzed to optimize the shortcomings and deficiencies of the existing question-and-answer (Q&A) machines and solve the problems of financial students' difficulty in answering questions. Firstly, the difficulties and needs of students in answering questions are understood. Secondly, the traditional algorithm principle by the Q&A system is introduced and analyzed, and the problems and defects existing in the traditional Q&A system are summarized. On this basis, deep learning algorithms are introduced, the long short-term memory (LSTM) neural network and convolutional neural network (CNN) are combined, and a Q&A system by long short-term memory-convolutional neural network (LSTM-CNN) is proposed, the gated recurrent unit (GRU) attention mechanism is introduced, and the algorithm is optimized. Finally, the design experiments to determine the nearest parameters of the neural network algorithm and verify the effectiveness of the algorithm are carried out. The results show that for the LSTM-CNN, the effect is the best when dropout = 0.5. After introducing the attention mechanism optimization, the effect is the best when dropout = 0.6. The test results of the comparison between the recommended algorithm and the traditional Q&A model algorithm show that the LSTM-CNN algorithm maintains the ability of the LSTM algorithm to arrange information in chronological order. After being combined with the CNN algorithm, the language features of the sentence can be extracted more deeply, the semantic feature information can be captured more accurately from the sentence, and better performance can be maintained when processing more complex sentences. The introduction of a BANet can simultaneously obtain the past and future information so that the algorithm can more appropriately combine it with the context to retrieve the semantic features, and the effectiveness of the model has been greatly improved. The research results have played an optimizing role in improving the Q&A effect of finance and economics teaching and provided a reference for research in related fields.
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10
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Bento M, Fantini I, Park J, Rittner L, Frayne R. Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets. Front Neuroinform 2022; 15:805669. [PMID: 35126080 PMCID: PMC8811356 DOI: 10.3389/fninf.2021.805669] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 12/27/2021] [Indexed: 12/22/2022] Open
Abstract
Large, multi-site, heterogeneous brain imaging datasets are increasingly required for the training, validation, and testing of advanced deep learning (DL)-based automated tools, including structural magnetic resonance (MR) image-based diagnostic and treatment monitoring approaches. When assembling a number of smaller datasets to form a larger dataset, understanding the underlying variability between different acquisition and processing protocols across the aggregated dataset (termed “batch effects”) is critical. The presence of variation in the training dataset is important as it more closely reflects the true underlying data distribution and, thus, may enhance the overall generalizability of the tool. However, the impact of batch effects must be carefully evaluated in order to avoid undesirable effects that, for example, may reduce performance measures. Batch effects can result from many sources, including differences in acquisition equipment, imaging technique and parameters, as well as applied processing methodologies. Their impact, both beneficial and adversarial, must be considered when developing tools to ensure that their outputs are related to the proposed clinical or research question (i.e., actual disease-related or pathological changes) and are not simply due to the peculiarities of underlying batch effects in the aggregated dataset. We reviewed applications of DL in structural brain MR imaging that aggregated images from neuroimaging datasets, typically acquired at multiple sites. We examined datasets containing both healthy control participants and patients that were acquired using varying acquisition protocols. First, we discussed issues around Data Access and enumerated the key characteristics of some commonly used publicly available brain datasets. Then we reviewed methods for correcting batch effects by exploring the two main classes of approaches: Data Harmonization that uses data standardization, quality control protocols or other similar algorithms and procedures to explicitly understand and minimize unwanted batch effects; and Domain Adaptation that develops DL tools that implicitly handle the batch effects by using approaches to achieve reliable and robust results. In this narrative review, we highlighted the advantages and disadvantages of both classes of DL approaches, and described key challenges to be addressed in future studies.
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Affiliation(s)
- Mariana Bento
- Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Calgary Image Processing and Analysis Centre, Foothills Medical Centre, Calgary, AB, Canada
- *Correspondence: Mariana Bento
| | - Irene Fantini
- School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
| | - Justin Park
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Calgary Image Processing and Analysis Centre, Foothills Medical Centre, Calgary, AB, Canada
- Radiology and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Leticia Rittner
- School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
| | - Richard Frayne
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Calgary Image Processing and Analysis Centre, Foothills Medical Centre, Calgary, AB, Canada
- Radiology and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, AB, Canada
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Automatic Quality Control in Lung X-Ray Imaging with Deep Learning. COMPUTATIONAL MATHEMATICS AND MODELING 2021. [PMCID: PMC8632564 DOI: 10.1007/s10598-021-09539-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The development of deep learning and its growing application in medical diagnosis have focused the attention on automatic control of image quality for neural-network medical image analysis algorithms. This article presents a method for automatic determination of the hardness (penetration) of lung X-ray images using standard criteria from chest X-ray diagnosis. The proposed method can be applied to automatically filter images by hardness (penetration) level and to detect low-quality images, thus facilitating the creation of high-quality data sets and increasing the efficiency of neural-network approaches to the analysis of lung X-ray images.
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