  Pathology detection and delineation enables the automatic interpretation of
medical scans such as chest X-rays while providing a high level of
explainability to support radiologists in making informed decisions. However,
annotating pathology bounding boxes is a time-consuming task such that large
public datasets for this purpose are scarce. Current approaches thus use weakly
supervised object detection to learn the (rough) localization of pathologies
from image-level annotations, which is however limited in performance due to
the lack of bounding box supervision. We therefore propose anatomy-driven
pathology detection (ADPD), which uses easy-to-annotate bounding boxes of
anatomical regions as proxies for pathologies. We study two training
approaches: supervised training using anatomy-level pathology labels and
multiple instance learning (MIL) with image-level pathology labels. Our results
show that our anatomy-level training approach outperforms weakly supervised
methods and fully supervised detection with limited training samples, and our
MIL approach is competitive with both baseline approaches, therefore
demonstrating the potential of our approach.
