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

Abstract

BackgroundLumbar 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. MethodWe propose SeCoFixMatch, an innovative approach that seamlessly integrates semantic contrast and uncertainty-aware pseudo labeling into semi-supervised learning. Semantic contrast constraints the semantic consistency between labeled and unlabeled images. Pseudo labels are generated by combining predictive confidence and uncertainty, with uncertainty computing by optimizing the Kullback-Leibler (KL) loss between predictive and target Dirichlet distribution. ResultsComparison with other semi-supervised models and ablation experiment with varying labeled data demonstrate the effectiveness and generalization of proposed model. Notably, SeCoFixMatch, trained with just 40 labels, outperforms the baseline model trained with 200 labels, reducing the annotation effort by a remarkable 80%. ConclusionsProposed pseudo labeling algorithm generates more precise pseudo labels for semantic contrastive learning and semantic contrastive learning facilitates better feature representation, thereby further improving the prediction accuracy of pseudo label. The mutual reinforcement of pseudo labeling and semantic contrast constraints boosts the performance of semi-supervised algorithm.