SNR-Adaptive Unified Diffusion for Multi-Task Medical Image Segmentation
Clinical cardiac imaging pipelines currently deploy separate models for each dataset and modality, incurring redundant training costs and precluding knowledge sharing across anatomically related tasks. Consolidating semi-supervised learning, unsupervised domain adaptation, and domain generalisation into one model is therefore a practical necessity, yet naive joint training exposes a fundamental barrier: conflicting label semantics between datasets collapse LA Dice from 90.31\% to 83.38\%, while gradient imbalance across tasks of unequal complexity suppresses the weaker tasks throughout training. We present UniT-Diff, a unified diffusion segmentation framework that resolves these conflicts...
| Authors | Jiahao Liu, Hang Wei, Shuai Wu |
|---|---|
| Primary category | cs.CV |
| Published | 3 July 2026 |
| Last updated at source | 3 July 2026 |
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