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arXiv:2610.34884v1 Announce Type: new Abstract: Reusing pretrained medical-imaging models offers an alternative to training from scratch. A model trained for the same task may perform poorly at a new...
The article reviews foundation models (FMs) in medical image analysis, highlighting their shift from task‑specific AI to generalized representations learned from large datasets. It categorizes studies into vision‑only foundation models (VFMs) and vision‑language foundation models (VLFMs), mapping their architectures, training strategies, and clinical applications. The review includes quantitative analyses of dataset use, performance trends, and discusses challenges such as generalization, scalability, evaluation, fairness, and deployment, while proposing future research directions for robustness, interpretability, and clinical integration.
arXiv:2606. 13211v1 Announce Type: new Abstract: AI systems are being deployed across medical imaging faster than their failure modes are understood.
arXiv:2602. 14010v2 Announce Type: replace-cross Abstract: Pathology foundation models (PFMs) generalize well across computational pathology tasks but remain costly for gigapixel whole-slide image analysis.
arXiv:2609.10001v1 Announce Type: new Abstract: Few-shot medical image segmentation (FSMIS) seeks to delineate unseen structures from a small support set, but its standard formulation fixes task-defi...
arXiv:2607. 07219v1 Announce Type: cross Abstract: Vision foundation models (VFMs) are increasingly being developed for radiological imaging, yet their definition, development and evaluation remain heterogeneous.