REMIND: Rethinking Medical High-Modality Learning under Missingness--A Long-Tailed Distribution Perspective
arXiv:2603.00046v1 Announce Type: new Abstract: Medical multi-modal learning is critical for integrating information from a large set of diverse modalities. However, when leveraging a high number of modalities in real clinical applications, it is often impractical to obtain full-modality observations for every patient due to data collection constraints, a problem we refer to as 'High-Modality Learning under Missingness'. In this study, we identify that such missingness inherently induces an exponential growth in possible modality combinations, followed by long-tail distributions of modality combinations due to varying modality availability. While prior work overlooked this critical phenomenon, we find this long-tailed distribution leads to significant underperformance on tail modality combination groups. Our empirical analysis attributes this problem to two fundamental issues: 1) gradient inconsistency, where tail groups' gradient updates diverge from the overall optimization directio
arXiv:2603.00046v1 Announce Type: new Abstract: Medical multi-modal learning is critical for integrating information from a large set of diverse modalities. However, when leveraging a high number of modalities in real clinical applications, it is often impractical to obtain full-modality observations for every patient due to data collection constraints, a problem we refer to as 'High-Modality Learning under Missingness'. In this study, we identify that such missingness inherently induces an exponential growth in possible modality combinations, followed by long-tail distributions of modality combinations due to varying modality availability. While prior work overlooked this critical phenomenon, we find this long-tailed distribution leads to significant underperformance on tail modality combination groups. Our empirical analysis attributes this problem to two fundamental issues: 1) gradient inconsistency, where tail groups' gradient updates diverge from the overall optimization direction; 2) concept shifts, where each modality combination requires distinct fusion functions. To address these challenges, we propose REMIND, a unified framework that REthinks MultImodal learNing under high-moDality missingness from a long-tail perspective. Our core idea is to propose a novel group-specialized Mixture-of-Experts architecture that scalably learns group-specific multi-modal fusion functions for arbitrary modality combinations, while simultaneously leveraging a group distributionally robust optimization strategy to upweight underrepresented modality combinations. Extensive experiments on real-world medical datasets show that our framework consistently outperforms state-of-the-art methods, and robustly generalizes across various medical multi-modal learning applications under high-modality missingness.
Executive Summary
This article proposes REMIND, a novel framework addressing high-modality learning under missingness in medical multi-modal applications. The authors identify long-tailed distributions of modality combinations and their associated challenges, including gradient inconsistency and concept shifts. REMIND employs a group-specialized Mixture-of-Experts architecture and a group distributionally robust optimization strategy to upweight underrepresented modality combinations. Empirical analysis demonstrates REMIND's superior performance and robust generalization across medical multi-modal learning applications. The REMIND framework has the potential to significantly improve medical diagnosis and treatment outcomes in real-world clinical settings.
Key Points
- ▸ High-modality learning under missingness is a critical challenge in medical multi-modal applications.
- ▸ Long-tailed distributions of modality combinations lead to significant underperformance in tail modality combination groups.
- ▸ REMIND addresses these challenges through a group-specialized Mixture-of-Experts architecture and a group distributionally robust optimization strategy.
Merits
Strength in Addressing Long-Tailed Distributions
The authors' identification of long-tailed distributions as a critical issue in high-modality learning under missingness is a significant contribution. REMIND's approach effectively addresses this challenge, leading to improved performance and robust generalization.
Improvements in Medical Diagnosis and Treatment
REMIND's potential to improve medical diagnosis and treatment outcomes in real-world clinical settings is a significant practical implication of the framework.
Demerits
Limitation in Handling Categorical Modalities
The authors assume continuous modalities and do not address potential issues with categorical modalities, which may be a limitation in certain medical applications.
Need for Further Evaluation in Real-World Settings
While REMIND demonstrates impressive performance in controlled experiments, further evaluation in real-world clinical settings is necessary to fully assess its practical implications.
Expert Commentary
The REMIND framework is a significant contribution to the field of medical multi-modal learning, addressing a critical challenge in high-modality learning under missingness. The authors' use of a group-specialized Mixture-of-Experts architecture and a group distributionally robust optimization strategy is innovative and effective. While there are limitations to the framework, particularly in handling categorical modalities and the need for further evaluation in real-world settings, REMIND has the potential to significantly improve medical diagnosis and treatment outcomes. As such, it is essential to continue exploring and refining this framework to realize its full potential.
Recommendations
- ✓ Future research should focus on extending REMIND to handle categorical modalities and evaluate its performance in real-world clinical settings.
- ✓ The development of policy and regulatory frameworks to support the adoption of multi-modal learning approaches in medical applications is crucial to ensure data sharing and collaboration among healthcare providers and researchers.