MedCore achieves 60% parameter and 58.4% FLOP reduction on MedSAM with Dice 0.9549 and preserved boundary metrics via dual-intervention pruning and a new boundary leverage principle.
I- MedSAM: Implicit medical image segmentation with segment anything
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PEFT-MedSAM adapts MedSAM by training only its mask decoder on ISIC 2018 skin lesion data, achieving Dice 0.9411 and outperforming U-Net (0.8715) and zero-shot MedSAM (0.8997), with PH2 validation (0.9467) and 98.27% Grad-CAM pointing accuracy.
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MedCore: Boundary-Preserving Medical Core Pruning for MedSAM
MedCore achieves 60% parameter and 58.4% FLOP reduction on MedSAM with Dice 0.9549 and preserved boundary metrics via dual-intervention pruning and a new boundary leverage principle.
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PEFT-MedSAM: Efficient Fine-Tuning of Medical Foundation Models for Explainable Skin Lesion Segmentation
PEFT-MedSAM adapts MedSAM by training only its mask decoder on ISIC 2018 skin lesion data, achieving Dice 0.9411 and outperforming U-Net (0.8715) and zero-shot MedSAM (0.8997), with PH2 validation (0.9467) and 98.27% Grad-CAM pointing accuracy.