LA Train
LA数据集半监督训练

Boosting Semi-Supervised Medical Image Segmentation Through Inter-Instance Information Complementarity
专家标注数据的获取仍然是医学图像分割的关键瓶颈

Background Matters A Cross-View Bidirectional Modeling Framework for Semi-Supervised Medical Image Segmentation
半监督医学图像分割(SSMIS)利用

Balancing Multi-Target Semi-Supervised Medical Image Segmentation With Collaborative Generalist and Specialists
尽管当前的半监督模型在单个医学目标分割任务中表现优异

CrossMatch Enhance Semi-Supervised Medical Image Segmentation With Perturbation Strategies and Knowledge Distillation
半监督学习医学图像分割提出了一个独特的挑战

Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation
半监督分割在医学成像中仍然具有挑战性

Cross-View Mutual Learning for Semi-Supervised Medical Image Segmentation
半监督医学图像分割因其减轻人工标注负担





