SMILE: Self-Explainable Multimodal Information Bottleneck for Medical Diagnosis

2026-09-04

AI Summary

本文通过信息瓶颈框架提出了一种自解释多模态诊断方法，优化预测性能和模ality特定解释性，提高医疗诊断的透明度和准确性。

Abstract

Explainability is increasingly seen as a crucial requirement in AI-based medical diagnosis, particularly in safety-critical clinical decision-making. Most existing explainability methods in healthcare operate in a post-hoc manner and are predominantly designed for unimodal data, which limits their applicability in increasingly prevalent multimodal diagnostic settings. This paper addresses the problem of self-explainable multimodal diagnosis by formulating it within the information bottleneck (IB) framework. We propose a unified learning paradigm that jointly optimizes predictive performance and modality-specific explainability by identifying the most informative elements inside each modality that contribute to diagnostic decisions. To enable tractable and stable optimization, we employ a matrix-based Renyi's $α$-order entropy functional under the assumption of sufficiently expressive encoders. Extensive experiments on representative medical datasets spanning heterogeneous modalities demonstrate that the proposed method consistently achieves strong diagnostic performance, including an absolute accuracy improvement of 9.1 percentage points on the iCTCF dataset. Moreover, the learned explanations provide transparent and modality-aware insights into feature relevance, thereby improving both the explainability and generalization.

Problem

• Explainability
• Multimodal Diagnosis
• Information Bottleneck
• Medical AI
• Clinical Decision-Making

Innovation

• self-explainable
• multimodal information bottleneck
• matrix-based Renyi's α-order entropy
• predictive performance and explainability
• medical diagnosis