  Visual explanation methods have an important role in the prognosis of the
patients where the annotated data is limited or not available. 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 a highly innovative algorithm to quantifying
lesions in the lungs caused by the Covid-19 with high accuracy and robustness
without using dense segmentation labels. Inspired by the information bottleneck
concept, we mask the neural network representation with noise to find out
important regions. This approach overcomes the drawbacks of commonly used
Grad-Cam and its derived algorithms. 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 and robust severity estimation than the similar
attribution methods.
