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.
Medical Image Analysis 82, 102616
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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.