  Neural networks have demonstrated remarkable performance in classification
and regression tasks on chest X-rays. In order to establish trust in the
clinical routine, the networks' prediction mechanism needs to be interpretable.
One principal approach to interpretation is feature attribution. Feature
attribution methods identify the importance of input features for the output
prediction. Building on Information Bottleneck Attribution (IBA) method, for
each prediction we identify the chest X-ray regions that have high mutual
information with the network's output. Original IBA identifies input regions
that have sufficient predictive information. We propose Inverse IBA to identify
all informative regions. Thus all predictive cues for pathologies are
highlighted on the X-rays, a desirable property for chest X-ray diagnosis.
Moreover, we propose Regression IBA for explaining regression models. Using
Regression IBA we observe that a model trained on cumulative severity score
labels implicitly learns the severity of different X-ray regions. Finally, we
propose Multi-layer IBA to generate higher resolution and more detailed
attribution/saliency maps. We evaluate our methods using both human-centric
(ground-truth-based) interpretability metrics, and human-independent feature
importance metrics on NIH Chest X-ray8 and BrixIA datasets. The Code is
publicly available.
