Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis
This addresses the need for reliable visual explanations in medical diagnosis and prognosis, particularly where annotated data is scarce, though it appears incremental as it builds on existing attribution methods.
The paper tackles the problem of unreliable gradient-based attribution methods for visual explanations in medical imaging by introducing a robust algorithm that minimizes information flow while maintaining classifier predictions, demonstrating high accuracy and robustness in quantifying Covid-19 lung lesions without dense segmentation labels.
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.