A domain knowledge powered hybrid regularization strategy for semi-supervised breast cancer diagnosis

Semi-supervised learning has attracted much attention in medical image analysis as annotating medical images is substantially difficult. Existing semi-supervised learning methods mainly utilize some regularization strategies to encourage a network to make similar predictions for each sample under various perturbations (e.g. rotating and flipping). However, as these strategies are originally designed for natural images, they do not fully utilize the specific attributes in the medical domain. In this paper, we propose DK-HRS, a domain knowledge-powered hybrid regularization strategy for semi-supervised breast cancer diagnosis. More specifically, DK-HRS generates four types of perturbed samples, namely, domain knowledge-augmented samples, weakly and strongly-augmented samples, and virtual adversarial samples. These perturbed samples represent transformations of medical images from different aspects. DK-HRS utilizes the FixMatch, a popular consistency regularization method as the backbone, and integrates two additional modules: a virtual adversarial training module, and a domain knowledge-based contrastive learning module. Experimental results on four breast ultrasound datasets demonstrate that, by incorporating medical domain knowledge, DK-HRS achieves superior performance and outperforms some state-of-the-art semi-supervised methods by a large margin.