CSCS selects initial annotation samples for 3D medical segmentation by combining self-supervised typicality and reconstruction uncertainty through a closed-form pacing rule based on the Difficulty-Coverage Ratio.
arXiv preprint arXiv:2508.03441 (2025)
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Dataset-Aware Cold-Start Active Learning for Annotation-Efficient 3D Medical Image Segmentation
CSCS selects initial annotation samples for 3D medical segmentation by combining self-supervised typicality and reconstruction uncertainty through a closed-form pacing rule based on the Difficulty-Coverage Ratio.