Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures. (arXiv:2208.08605v1 [cs.CV])

Convolutional Neural Networks (CNNs) have achieved state-of-the-art
performance for medical image segmentation, yet need plenty of manual
annotations for training. Semi-Supervised Learning (SSL) methods are promising
to reduce the requirement of annotations, but their performance is still
limited when the dataset size and the number of annotated images are small.
Leveraging existing annotated datasets with similar anatomical structures to
assist training has a potential for improving the model's performance. However,
it is further challenged by the cross-anatomy domain shift due …

arxiv cv learning segmentation semi-supervised semi-supervised learning supervised learning