Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis

TL;DR

This paper introduces a robust visual explanation method based on information bottleneck principles for medical diagnosis and prognosis, demonstrating high accuracy and stability in lung lesion quantification without segmentation labels.

Contribution

The study proposes a novel information bottleneck attribution method that improves robustness and reliability of visual explanations in medical imaging, outperforming Grad-CAM.

Findings

Provides stable severity estimation compared to existing methods

Achieves high accuracy in quantifying Covid-19 lung lesions

Overcomes limitations of gradient-based attribution methods

Abstract

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.