Quality-Guided Semi-Supervised Learning for Medical Image Segmentation 事件

Quality-Guided Semi-Supervised Learning for Medical Image Segmentation arXiv:2606.01753v1 Announce Type: new Abstract: Training accurate medical image segmentation models requires large amounts of densely annotated data, which is costly and time-consuming to obtain. Semi-supervised learning (SSL) alleviates this by learning from both abundant unlabeled data and limited labeled data. However, most modern SSL methods rely on pseudolabels for unlabeled data, and typically assess their reliability