Researchers from CUHK and Harvard Medical School built a training paradigm where the in-training specialist autonomously crafts prompts for a frozen SAM generalist, which returns noisy-but-useful labels across three orthogonal views — while active learning selects the most informative scans…
GM-ABS: SAM-Driven Active Barely Supervised 3D Medical Image Segmentation Read More »
A team at the Chinese Academy of Sciences built a framework that achieves segmentation accuracy within 3% of fully-supervised models across ten diverse medical datasets — using exactly one manually annotated image per task. SAM2 does the label propagation; a…
YoloSeg: One Labeled Image Is All You Need for Medical Image Segmentation Read More »
Medical image segmentation has a labeling problem that computer vision in general does not face nearly as badly. Drawing a box around a cat in a photo takes seconds. Tracing the exact boundary of a pancreas across dozens of CT…
SDCL: Two Students Learning From Each Other’s Mistakes Fix a Blind Spot in Segmentation Read More »