Semi‐supervised medical image segmentation network based on mutual learning

BACKGROUND: Semi-supervised learning provides an effective means to address the challenge of insufficient labeled data in medical image segmentation tasks. However, when a semi-supervised segmentation model is overfitted and exhibits cognitive bias, its performance will deteriorate. Errors stemming from cognitive bias can quickly amplify and become difficult to correct during the training process of neural networks, resulting in the continuous accumulation of erroneous knowledge. PURPOSE: To address the issue of error accumulation, a novel learning strategy is required to enhance the accuracy of medical image segmentation. METHODS: This paper proposes a semi-supervised medical image segmentation network based on mutual learning (MLNet) to alleviate the issue of continuous accumulation of erroneous knowledge. The MLNet adopts a teacher-student network as the backbone framework, training student and teacher networks on labeled data and mutually updating network parameter weights, enabling the two models to learn from each other. Additionally, an image partial exchange algorithm (IPE) as an appropriate perturbation addition method is proposed to reduce the introduction of erroneous information and the disruption to the contextual information of the image. RESULTS: In the 10% labeled experiment on the ACDC dataset, our Dice coefficient reached 89.48%, a 9.28% improvement over the baseline model. In the 10% labeled experiment on the BraTS2019 dataset, the proposed method still performs exceptionally well, achieving 84.56%, surpassing other comparative methods. CONCLUSIONS: Compared with other methods, experimental results demonstrate that our approach achieves optimal performance across all metrics, proving its effectiveness and reliability.