Pan-FM learns balanced representations across seven organs by adaptively masking dominant organs during pre-training, yielding stronger disease prediction and missing-organ robustness than single-organ or naive multimodal baselines on UK Biobank.
Triad: Vision foundation model for 3d magnetic resonance imaging
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ASSFT combines active test-time sample selection via diversified knowledge divergence and anatomical segmentation difficulty with selective semi-supervised fine-tuning to adapt medical vision foundation models for volumetric segmentation under limited annotation budgets without source data access.
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Pan-FM: A Pan-Organ Foundation Model with Saliency-Guided Masking for Missing Robustness
Pan-FM learns balanced representations across seven organs by adaptively masking dominant organs during pre-training, yielding stronger disease prediction and missing-organ robustness than single-organ or naive multimodal baselines on UK Biobank.
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Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning
ASSFT combines active test-time sample selection via diversified knowledge divergence and anatomical segmentation difficulty with selective semi-supervised fine-tuning to adapt medical vision foundation models for volumetric segmentation under limited annotation budgets without source data access.