Quality-driven deep cross-supervised learning network for semi-supervised medical image segmentation

Abstract

Semi-supervised medical image segmentation presents a compelling approach to streamline large-scale image analysis, alleviating annotation burdens while maintaining comparable performance. Despite recent strides in cross-supervised training paradigms, challenges persist in addressing sub-network disagreement and training efficiency and reliability. In response, our paper introduces a novel cross-supervised learning framework, Quality-driven Deep Cross-supervised Learning Network (QDC-Net). QDC-Net incorporates both an evidential sub-network and an vanilla sub-network, leveraging their complementary strengths to effectively handle disagreement. To enable the reliability and efficiency of semi-supervised training, we introduce a real-time quality estimation of the model's segmentation performance and propose a directional cross-training approach through the design of directional weights. We further design a truncated form of sample-wise loss weighting to mitigate the impact of inaccurate predictions and collapsed samples in semi-supervised training. Extensive experiments on LA and Pancreas-CT datasets demonstrate that QDC-Net surpasses other state-of-the-art methods in semi-supervised medical image segmentation. Code release is available at https://github.com/Medsemiseg.

Keywords: Cross-supervised learning; Evidential learning; Medical image segmentation; Semi-supervised learning.