Seeking Necessary and Sufficient Information from Multimodal Medical Data

2026-02-27

AI Summary

This work addresses the limited robustness of existing multimodal medical learning models under missing modalities, which often stems from their failure to learn features that are both necessary and sufficient for accurate prediction. To this end, the study introduces the probabilistic necessity and sufficiency (PNS) framework into multimodal medical learning for the first time, proposing a novel paradigm that disentangles multimodal representations into modality-invariant and modality-specific components. It further formulates computable PNS-based objective functions for each component to encourage the learning of more discriminative and causally essential features. Extensive experiments on both synthetic and real-world medical datasets demonstrate that the proposed method significantly improves predictive performance and enhances robustness against missing modalities.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Learning multimodal representations from medical images and other data sources can provide richer information for decision-making. While various multimodal models have been developed for this, they overlook learning features that are both necessary (must be present for the outcome to occur) and sufficient (enough to determine the outcome). We argue learning such features is crucial as they can improve model performance by capturing essential predictive information, and enhance model robustness to missing modalities as each modality can provide adequate predictive signals. Such features can be learned by leveraging the Probability of Necessity and Sufficiency (PNS) as a learning objective, an approach that has proven effective in unimodal settings. However, extending PNS to multimodal scenarios remains underexplored and is non-trivial as key conditions of PNS estimation are violated. We address this by decomposing multimodal representations into modality-invariant and modality-specific components, then deriving tractable PNS objectives for each. Experiments on synthetic and real-world medical datasets demonstrate our method's effectiveness. Code will be available on GitHub.

Problem

• multimodal medical data
• necessary and sufficient features
• Probability of Necessity and Sufficiency
• model robustness
• missing modalities

Innovation

• necessary and sufficient information
• multimodal medical learning
• Probability of Necessity and Sufficiency (PNS)
• modality-invariant representation
• robustness to missing modalities