  The scarcity of richly annotated medical images is limiting supervised deep
learning based solutions to medical image analysis tasks, such as localizing
discriminatory radiomic disease signatures. Therefore, it is desirable to
leverage unsupervised and weakly supervised models. Most recent weakly
supervised localization methods apply attention maps or region proposals in a
multiple instance learning formulation. While attention maps can be noisy,
leading to erroneously highlighted regions, it is not simple to decide on an
optimal window/bag size for multiple instance learning approaches. In this
paper, we propose a learned spatial masking mechanism to filter out irrelevant
background signals from attention maps. The proposed method minimizes mutual
information between a masked variational representation and the input while
maximizing the information between the masked representation and class labels.
This results in more accurate localization of discriminatory regions. We tested
the proposed model on the ChestX-ray8 dataset to localize pneumonia from chest
X-ray images without using any pixel-level or bounding-box annotations.
