Weakly Supervised Object Detection in Chest X-Rays with Differentiable
  ROI Proposal Networks and Soft ROI Pooling
Philip Müller Felix Meissen Georgios Kaissis Daniel Rueckert
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