Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation

Understand

Supervised deep learning-based methods yield accurate results for medical image segmentation.

• However, they require large labeled datasets for this, and obtaining them is a laborious task that requires clinical expertise.
• Semi/self-supervised learning-based approaches address this limitation by exploiting unlabeled data along with limited annotated data.
• Recent self-supervised learning methods use contrastive loss to learn good global level representations from unlabeled images and achieve high performance in classification tasks on popular natural image datasets like ImageNet.