Semantic contrast with uncertainty-aware pseudo label for lumbar semi-supervised classification

Lumbar disc herniation (LDH) is a prevalent spinal disease that can result in severe pain, with Magnetic resonance imaging (MRI) serving as a commonly diagnostic tool. However, annotating numerous MRI images, necessary for deep learning based LDH diagnosis, can be challenging and labor-intensive. Semi-supervised learning, mainly utilizing pseudo labeling and consistency regularization, can leverage limited labeled images and abundant unlabeled images. However, consistency regularization solely focuses on maintaining the semantic consistency of transformed unlabeled data but fails to utilize the semantic information from labeled data to guide the unlabeled data, and additionally, pseudo labeling is prone to confirmation bias.