An unsupervised pipeline that uses CLIP to label slices, CAM to localize tumors, SAM to generate pseudo-masks, and self-training with similarity filtering reaches 85.6% Dice on BraTS2020 whole-tumor segmentation.
Deep learning models and traditional automated techniques for brain tumor segmentation in MRI: a review,
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CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentation
An unsupervised pipeline that uses CLIP to label slices, CAM to localize tumors, SAM to generate pseudo-masks, and self-training with similarity filtering reaches 85.6% Dice on BraTS2020 whole-tumor segmentation.