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In many image-guided clinical approaches, tumor segmentation is a fundamental and critical step for locating tumor involvement. However, the scarcity of annotated data and the low contrast of medical imaging techniques make it challenging to accurately segment tumors from surrounding tissues using supervised learning methods. To address these issues, we propose a patch-based contrastive semi-supervised learning framework with wavelet guidance and adaptive data augmentation (PaCaS-WAA). Specifically, we apply patch-based contrast to maintain high-quality segmentation results with limited labels. Moreover, to exploit the discriminative information about subtle boundaries, we use the wavelet domain guides UNet for more edge details. Besides, to increase the diversity of unlabelled data, we propose an adaptive data augmentation strategy to augment the unlabelled data according to its Challenging Grade. Experimental results on two publicly available datasets of different modalities demonstrate that our method consistently outperform the state-of-the-art semi-supervised segmentation methods.
Xiong et al. (Mon,) studied this question.