HarMoE: Multi-Source Chest Radiograph Pretraining with Dataset-Disentangled Experts
This work proposes HarMoE, a dataset-aware mixture-of-experts framework that learns shared cross-dataset medical semantics while confining source-specific variation to lightweight residual experts in deeper decoder layers, and trains in a unified disease vocabulary with masked multi-dataset supervision, enabling the mo...