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Seeking Necessary and Sufficient Information from Multimodal Medical Data

Boyu Chen, Weiye Bao, Junjie Liu, Michael Shen, Bo Peng, Paul Taylor, Zhu Li, Mengyue Yang

TL;DR

This work decomposes multimodal representations into modality-invariant and modality-specific components, then deriving tractable PNS objectives for each and argues 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.

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.

Seeking Necessary and Sufficient Information from Multimodal Medical Data

TL;DR

This work decomposes multimodal representations into modality-invariant and modality-specific components, then deriving tractable PNS objectives for each and argues 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.

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.
Paper Structure (12 sections, 1 theorem, 6 equations, 1 figure, 2 tables)

This paper contains 12 sections, 1 theorem, 6 equations, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Method
  4. Learning Complement Representations
  5. PNS for Modality-Invariant Representation
  6. PNS for Modality-Specific Representation
  7. Multimodal Representation Learning via PNS
  8. Experiments and Results
  9. Synthetic Multimodal Dataset Experiments
  10. Real-world Multimodal Medical Dataset Experiments
  11. Results and Discussion
  12. Conclusion

Key Result

lemma 1

Under monotonicity and exogeneity:

Figures (1)

  • Figure 1: Overview of the proposed MPNS framework. The figure shows the multimodal data generation process, a baseline decoupling model, and our proposed complement branch with PNS-based optimization.

Theorems & Definitions (3)

  • definition 1: Exogeneity pearl2009causality
  • definition 2: Monotonicity pearl2009causality
  • lemma 1: pearl2009causality