LongMoE: Longitudinal Multimodal Learning via Trajectory-Aware Mixture-of-Experts
Multimodal clinical learning is increasingly important for integrating diverse patient data, including imaging, text, and personalised health records. However, it faces two fundamental challenges: i) modality missingness, where arbitrary subsets of modalities are unavailable at a given patient visit, ii) longitudinal dynamics, where the diagnostic significance of an observation depends on the patient's evolving disease trajectory over time. Existing methods address these challenges in isolation: missing-modality frameworks treat each visit as an independent static snapshot and discard temporal context, while longitudinal models often assume complete modality availability and degrade under...
| Authors | Maxx Richard Rahman, Prakhar Kumar, Wolfgang Maass |
|---|---|
| Primary category | cs.LG |
| Published | 6 June 2026 |
| Last updated at source | 30 September 2026 |
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