Understanding Task Aggregation for Generalizable Ultrasound Foundation Models
Foundation models promise to unify multiple clinical tasks within a single framework, but recent ultrasound studies report that unified models can underperform task-specific baselines. We hypothesize that this degradation arises not from model capacity limitations, but from task aggregation strategies that ignore interactions between task heterogeneity and available training data scale. In this work, we systematically analyze when heterogeneous ultrasound tasks can be jointly learned without performance loss, establishing practical criteria for task aggregation in unified clinical imaging models. We introduce M2DINO, a multi-organ, multi-task framework built on DINOv3 with task-conditione...
| Authors | Fangyijie Wang, Tanya Akumu, Vien Ngoc Dang et al. |
|---|---|
| Primary category | eess.IV |
| Published | 18 March 2026 |
| Last updated at source | 22 May 2026 |
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