Improving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations

TL;DR

This paper enhances interpretability in whole slide imaging classification by combining gradient-based explanations, feature visualization, and multiple instance learning, leading to more transparent and accurate medical diagnoses.

Contribution

It introduces a novel interpretability approach for WSI classification that improves tile-level explanations and overall performance using gradient methods and feature visualization.

Findings

Improved slide-level heat-map interpretability.

Enhanced tile-level classification accuracy by over 29%.

Validated approach with pathologists' feedback.

Abstract

Deep learning methods are widely used for medical applications to assist medical doctors in their daily routines. While performances reach expert's level, interpretability (highlight how and what a trained model learned and why it makes a specific decision) is the next important challenge that deep learning methods need to answer to be fully integrated in the medical field. In this paper, we address the question of interpretability in the context of whole slide images (WSI) classification. We formalize the design of WSI classification architectures and propose a piece-wise interpretability approach, relying on gradient-based methods, feature visualization and multiple instance learning context. We aim at explaining how the decision is made based on tile level scoring, how these tile scores are decided and which features are used and relevant for the task. After training two WSI classification architectures on Camelyon-16 WSI dataset, highlighting discriminative features learned, and validating our approach with pathologists, we propose a novel manner of computing interpretability slide-level heat-maps, based on the extracted features, that improves tile-level classification performances by more than 29% for AUC.