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Foundation models are general-purpose, adaptable deep learning models trained on vast amounts of data. Despite unprecedented performance in language and vision tasks, their utility for predicting patient outcomes remains to be ex...

To address the data scarcity bottleneck in deep learning (DL) dose prediction by establishing a foundation model-based transfer learning framework. We aim to demonstrate that adapting large-scale public priors to institutional da...

Low-count PET acquisition and inter-radiotracer translation offer effective strategies to reduce radiation dose and mitigate tracer availability constraints. Recent self-supervised learning (SSL) foundation models (e.g., DINOv3)...