Home
Deutsch
Advanced Search
Browse by ...
Publication overview
Contact persons
Help
Postprint
DOI
 |
Open Access Gold (Paid Option) |
as soon as is submitted to ZB.
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)
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.
Altmetric
Additional Metrics?
Edit extra informations
Login
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
ISSN (print) / ISBN
0278-0062
e-ISSN
1558-254X
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publishing Place
New York, NY [u.a.]
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute for Machine Learning in Biomed Imaging (IML)