MDPI Acta Medica Okayama 2075-4418 13 8 2023 Deep Active Learning for Automatic Mitotic Cell Detection on HEp-2 Specimen Medical Images 1416 EN Asaad Anaam Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University Mugahed A. Al-antari Department of Artificial Intelligence, College of Software & Convergence Technology, Daeyang AI Center, Sejong University Jamil Hussain Department of Data Science, College of Software & Convergence Technology, Daeyang AI Center, Sejong University Nagwan Abdel Samee Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University Maali Alabdulhafith Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University Akio Gofuku Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University 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. No potential conflict of interest relevant to this article was reported.