[2603.00046] REMIND: Rethinking Medical High-Modality Learning under Missingness--A Long-Tailed Distribution Perspective

Computer Science > Machine Learning arXiv:2603.00046 (cs) [Submitted on 9 Feb 2026] Title:REMIND: Rethinking Medical High-Modality Learning under Missingness--A Long-Tailed Distribution Perspective Authors:Chenwei Wu, Zitao Shuai, Liyue Shen View a PDF of the paper titled REMIND: Rethinking Medical High-Modality Learning under Missingness--A Long-Tailed Distribution Perspective, by Chenwei Wu and 2 other authors View PDF HTML (experimental) 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...