MaFMatch: Semi‐Supervised Medical Image Segmentation Network Based on Mixed Data and Feature Augmentation

ABSTRACT Medical image segmentation plays a crucial role in biomedical engineering and computer‐aided medical systems. Fully supervised medical image segmentation algorithms have achieved significant performance improvements. However, these improvements often rely on large amounts of finely annotated data, while semi‐supervised medical image segmentation can better address this issue. In this work, we propose the MaFMatch model based on the principle of weak‐to‐strong consistency. It effectively addresses the limitations of current semi‐supervised medical image segmentation, such as noise generated by perturbation leading the model to learn in unfavorable directions, and simple feature perturbations being insufficient to explore a broader perturbation space. On the one hand, this approach introduces a mixed data perturbation flow to utilize all pixel information, making the model inclined to consider global semantic information rather than simply discarding unreliable pixels. On the other hand, to fully utilize feature perturbation information flow, we propose a feature augmentation perturbation scheme that simultaneously supplements and discards information from the original feature flow, enabling the model to effectively overcome the diminishing marginal returns brought about by multi‐branch perturbations. MaFMatch achieved an mDsc of 90.8 on the automatic cardiac diagnosis challenge (ACDC) dataset. It outperforms most methods across major metrics on both ACDC and LA datasets. The code is available at https://github.com/HandsomeRed/MaFMatch‐main .