Weakly Supervised Object Detection in Chest X-Rays with Differentiable ROI Proposal Networks and Soft ROI Pooling
This work addresses the problem of improving interpretability and usefulness of medical image classification for healthcare applications, but it is incremental as it adapts existing weakly supervised techniques to a specific domain.
The paper tackled disease localization in chest X-ray images using weakly supervised object detection, proposing Weakly Supervised ROI Proposal Networks (WSRPN) with a specialized ROI-attention module, and demonstrated that it outperforms existing methods in this task.
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://github.com/philip-mueller/wsrpn