  Visual explanation methods have an important role in the prognosis of the
patients where the annotated data is limited or unavailable. There have been
several attempts to use gradient-based attribution methods to localize
pathology from medical scans without using segmentation labels. This research
direction has been impeded by the lack of robustness and reliability. These
methods are highly sensitive to the network parameters. In this study, we
introduce a robust visual explanation method to address this problem for
medical applications. We provide an innovative visual explanation algorithm for
general purpose and as an example application, we demonstrate its effectiveness
for quantifying lesions in the lungs caused by the Covid-19 with high accuracy
and robustness without using dense segmentation labels. This approach overcomes
the drawbacks of commonly used Grad-CAM and its extended versions. The premise
behind our proposed strategy is that the information flow is minimized while
ensuring the classifier prediction stays similar. Our findings indicate that
the bottleneck condition provides a more stable severity estimation than the
similar attribution methods.
