ID | 65258 |
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Author |
Anaam, Asaad
Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University
Al-antari, Mugahed A.
Department of Artificial Intelligence, College of Software & Convergence Technology, Daeyang AI Center, Sejong University
Hussain, Jamil
Department of Data Science, College of Software & Convergence Technology, Daeyang AI Center, Sejong University
Abdel Samee, Nagwan
Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University
Alabdulhafith, Maali
Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University
Gofuku, Akio
Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University
Kaken ID
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Abstract | Identifying Human Epithelial Type 2 (HEp-2) mitotic cells is a crucial procedure in anti-nuclear antibodies (ANAs) testing, which is the standard protocol for detecting connective tissue diseases (CTD). Due to the low throughput and labor-subjectivity of the ANAs' manual screening test, there is a need to develop a reliable HEp-2 computer-aided diagnosis (CAD) system. The automatic detection of mitotic cells from the microscopic HEp-2 specimen images is an essential step to support the diagnosis process and enhance the throughput of this test. This work proposes a deep active learning (DAL) approach to overcoming the cell labeling challenge. Moreover, deep learning detectors are tailored to automatically identify the mitotic cells directly in the entire microscopic HEp-2 specimen images, avoiding the segmentation step. The proposed framework is validated using the I3A Task-2 dataset over 5-fold cross-validation trials. Using the YOLO predictor, promising mitotic cell prediction results are achieved with an average of 90.011% recall, 88.307% precision, and 81.531% mAP. Whereas, average scores of 86.986% recall, 85.282% precision, and 78.506% mAP are obtained using the Faster R-CNN predictor. Employing the DAL method over four labeling rounds effectively enhances the accuracy of the data annotation, and hence, improves the prediction performance. The proposed framework could be practically applicable to support medical personnel in making rapid and accurate decisions about the mitotic cells' existence.
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Keywords | medical HEp-2 specimen images
HEp-2 mitotic cell detection
deep active learning (DAL)
automatic data annotation
computer-aided detection (CAD)
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Published Date | 2023-04-14
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Publication Title |
Diagnostics
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Volume | volume13
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Issue | issue8
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Publisher | MDPI
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Start Page | 1416
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ISSN | 2075-4418
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Content Type |
Journal Article
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language |
English
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OAI-PMH Set |
岡山大学
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Copyright Holders | © 2023 by the authors.
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File Version | publisher
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DOI | |
Web of Science KeyUT | |
Related Url | isVersionOf https://doi.org/10.3390/diagnostics13081416
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License | https://creativecommons.org/licenses/by/4.0/
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Citation | Anaam, A.; Al-antari, M.; Hussain, J.; Abdel Samee, N.; Alabdulhafith, M.; Gofuku, A. Deep Active Learning for Automatic Mitotic Cell Detection on HEp-2 Specimen Medical Images. Diagnostics 2023, 13, 1416. https://doi.org/10.3390/diagnostics13081416
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