SMILE: Self-Explainable Multimodal Information Bottleneck for Medical Diagnosis
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 an
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- PossiblePossibly related (embedding) · 54%TIP12 Validation of a Multimodal Artificial Intelligence Prognostic Model in Early-Stage HR+/HER2− Breast Cancer - CancerNetwork →
- PossiblePossibly related (embedding) · 51%Limited benchmarks constrain the conclusions of a general-purpose versus clinical AI comparison - Nature →
- FuzzyOverlapping authors or contributors · 62%ultralytics/yolov5 →
“Shared author/contributor keys: choi”
- LinkedLinked via arxiv author · 85%Yuqing Yang →
“SMILE: Self-Explainable Multimodal Information Bottleneck for Medical Diagnosis”
- LinkedLinked via arxiv author · 85%Alexander Schmatz →
“SMILE: Self-Explainable Multimodal Information Bottleneck for Medical Diagnosis”
- LinkedLinked via arxiv author · 85%Zhaozhao Ma →
“SMILE: Self-Explainable Multimodal Information Bottleneck for Medical Diagnosis”
- LinkedLinked via arxiv author · 85%Changkyu Choi →
“SMILE: Self-Explainable Multimodal Information Bottleneck for Medical Diagnosis”
- LinkedLinked via arxiv author · 85%Robert Jenssen →
“SMILE: Self-Explainable Multimodal Information Bottleneck for Medical Diagnosis”