SMILE：用于医学诊断的自解释多模态信息瓶颈
SMILE: Self-Explainable Multimodal Information Bottleneck for Medical Diagnosis
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中文总结 AI 辅助
该研究针对现有医疗AI可解释方法多为事后单模态设计的局限，提出SMILE框架，通过信息瓶颈范式实现自解释多模态医学诊断，在iCTCF数据集准确率提升9.1个百分点，兼顾性能与可解释性。
中文摘要 AI 辅助
可解释性在基于AI的医学诊断中日益成为关键要求，尤其在安全关键的临床决策中。现有医疗领域的大多数可解释性方法采用事后方式，且主要针对单模态数据设计，这限制了其在日益普遍的多模态诊断场景中的适用性。本文在信息瓶颈（IB）框架内解决自解释多模态诊断问题，提出一种统一学习范式，通过识别各模态中对诊断决策最具信息价值的元素，联合优化预测性能与模态特定的可解释性。为实现可处理且稳定的优化，在编码器足够表达的假设下，采用基于矩阵的Renyi α阶熵泛函。在涵盖异质模态的代表性医学数据集上开展的大量实验表明，所提方法始终实现优异的诊断性能，其中iCTCF数据集上的绝对准确率提升9.1个百分点；此外，学习到的解释提供了透明且感知模态的特征相关性见解，从而提升可解释性与泛化能力。
英文摘要
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.
