Guest Editorial Special Issue on Advancements in Foundation Models for Medical Imaging
Abstract
Pretrained on massive datasets, Foundation Models (FMs) are revolutionizing medical imaging by offering scalable and generalizable solutions to longstanding challenges. This Special Issue on Advancements in Foundation Models for Medical Imaging presents FM-related works that explore the potential of FMs to address data scarcity, domain shifts, and multimodal integration across a wide range of medical imaging tasks, including segmentation, diagnosis, reconstruction, and prognosis. The included papers also examine critical concerns such as interpretability, efficiency, benchmarking, and ethics in the adoption of FMs for medical imaging. Collectively, the articles in this Special Issue mark a significant step toward establishing FMs as a cornerstone of next-generation medical imaging AI.
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