Dual Student Discrepancy Correction for Semi‐Supervised Medical Image Segmentation

ABSTRACT Semi‐supervised medical image segmentation (SSMIS) has proven to be an effective solution that leverages limited labelled data and abundant unlabeled data, thereby significantly reducing the labour and cost associated with manual annotation. However, most of the existing teacher‐student frameworks are prone to suffer from confirmation bias during training, adversely affecting the performance of SSMIS. To address this challenge, we propose the Dual Student Discrepancy Correction framework (DSDC), which extends the Mean Teacher (MT) framework by incorporating an additional student model with identical architecture but independently updated parameters. This design mitigates the parameter coupling issue that may arise when updating the teacher model via Exponential Moving Average (EMA) in conventional single‐student paradigms. Moreover, the prediction discrepancy between the two student models is leveraged for error detection and correction, enabling the network to identify and rectify its own cognitive biases, ultimately enhancing segmentation accuracy. Comprehensive experiments on two public benchmarks, an MRI dataset (LA) and a CT dataset (Pancreas‐NIH), reveal that our DSDC framework surpasses current State‐of‐the‐Art (SOTA) approaches across all evaluation metrics. These findings substantiate the framework's effectiveness in SSMIS tasks. Code is accessible at https://github.com/Sangfugui/DSDC .