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Müller, P.* ; Meissen, F.* ; Kaissis, G. ; Rueckert, D.*

Weakly supervised object detection in chest X-rays with differentiable ROI proposal networks and soft ROI pooling.

IEEE Trans. Med. Imaging, DOI: 10.1109/TMI.2024.3435015 (2024)
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Open Access Hybrid
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Weakly supervised object detection (WSup-OD) increases the usefulness and interpretability of image classification algorithms without requiring additional supervision. The successes of multiple instance learning in this task for natural images, however, do not translate well to medical images due to the very different characteristics of their objects (i.e. pathologies). In this work, we propose Weakly Supervised ROI Proposal Networks (WSRPN), a new method for generating bounding box proposals on the fly using a specialized region of interest-attention (ROI-attention) module. WSRPN integrates well with classic backbone-head classification algorithms and is end-to-end trainable with only image-label supervision. We experimentally demonstrate that our new method outperforms existing methods in the challenging task of disease localization in chest X-ray images. Code: https://anonymous.4open.science/r/WSRPN-DCA1.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Biomedical Imaging ; Chest X-ray ; Image Edge Detection ; Location Awareness ; Object Detection ; Object Detection ; Pathology Detection ; Proposals ; Task Analysis ; Weak Supervision ; X-ray Imaging
Language english
Publication Year 2024
HGF-reported in Year 2024
ISSN (print) / ISBN 0278-0062
e-ISSN 1558-254X
Journal IEEE Transactions on Medical Imaging
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Publishing Place New York, NY [u.a.]
Reviewing status Peer reviewed
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-507100-001
DOI 10.1109/TMI.2024.3435015
Scopus ID 85200234451
Erfassungsdatum 2024-09-04
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