University of Oulu

A. M. Raisuddin, H. H. Nguyen and A. Tiulpin, "Deep Semi-Supervised Active Learning for Knee Osteoarthritis Severity Grading," 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), Kolkata, India, 2022, pp. 1-5, doi: 10.1109/ISBI52829.2022.9761668

Deep semi-supervised active learning for knee osteoarthritis severity grading

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Author: Raisuddin, Abu Mohammed1; Nguyen, Huy Hoang1; Tiulpin, Aleksei1
Organizations: 1Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.6 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2023-03-01


This paper tackles the problem of developing active learning (AL) methods in the context of knee osteoarthritis (OA) diagnosis from X-ray images. OA is known to be a huge burden for society, and its associated costs are constantly rising. Automatic diagnostic methods can potentially reduce these costs, and Deep Learning (DL) methodology may be its key enabler. To date, there have been numerous studies on knee OA severity grading using DL, and all but one of them assume a large annotated dataset available for model development. In contrast, our study shows one can develop a knee OA severity grading model using AL from as little as 50 samples randomly chosen from a pool of unlabeled data. The main insight of this work is that the performance of AL improves when the model developer leverages the consistency regularization technique, commonly applied in semi-supervised learning.

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Series: IEEE International Symposium on Biomedical Imaging
ISSN: 1945-7928
ISSN-E: 1945-8452
ISSN-L: 1945-7928
ISBN: 978-1-6654-2923-8
ISBN Print: 978-1-6654-2924-5
Article number: 9761668
DOI: 10.1109/isbi52829.2022.9761668
Host publication: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)
Conference: IEEE International Symposium on Biomedical Imaging
Type of Publication: A4 Article in conference proceedings
Field of Science: 217 Medical engineering
3126 Surgery, anesthesiology, intensive care, radiology
Funding: This study is supported by the internal funds of the University of Oulu, Finland and the Finnish Center for Artificial Intelligence (FCAI).
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