Pairwise Similarity Regularization for Semi-supervised Graph Medical Image Segmentation
This work addresses distribution shift issues in medical image segmentation, which is an incremental improvement for enhancing semi-supervised learning efficiency in healthcare applications.
The paper tackles the problem of distributional shift between labeled and unlabeled data in semi-supervised medical image segmentation by proposing a pairwise similarity regularization method, resulting in an average improvement of over 10.66% on the ACDC dataset compared to advanced methods.
With fully leveraging the value of unlabeled data, semi-supervised medical image segmentation algorithms significantly reduces the limitation of limited labeled data, achieving a significant improvement in accuracy. However, the distributional shift between labeled and unlabeled data weakens the utilization of information from the labeled data. To alleviate the problem, we propose a graph network feature alignment method based on pairwise similarity regularization (PaSR) for semi-supervised medical image segmentation. PaSR aligns the graph structure of images in different domains by maintaining consistency in the pairwise structural similarity of feature graphs between the target domain and the source domain, reducing distribution shift issues in medical images. Meanwhile, further improving the accuracy of pseudo-labels in the teacher network by aligning graph clustering information to enhance the semi-supervised efficiency of the model. The experimental part was verified on three medical image segmentation benchmark datasets, with results showing improvements over advanced methods in various metrics. On the ACDC dataset, it achieved an average improvement of more than 10.66%.