Channel-wise joint disentanglement representation learning for B-mode and super-resolution ultrasound based CAD of breast cancer.

B-mode ultrasound (BUS) is widely used in breast cancer diagnosis, while the emerging super-resolution ultrasound (SRUS) provides microvascular information with high spatial resolution, which has shown great potential in improving breast cancer diagnosis. However, as a new ultrasound modality, its diagnosis remains highly dependent on the clinical experience of sonologists, highlighting the need for reliable computer-aided diagnosis (CAD) approaches. In this work, a novel dual-branch network with a Channel-Wise Joint Disentanglement Representation Learning (CW-JDRL) method is proposed for the multimodal ultrasound-based CAD of breast cancer, where one branch processes BUS and the other analyzes multimodal SRUS data. The CW-JDRL is implemented on the SRUS branch by grouping the final-layer network channels to capture both common and specific properties. It consists of two modules, namely Gradient-guided Disentanglement (GD) module and Gramian-based Contrastive Learning Disentanglement (GCLD) module. The former disentangles with gradient guidance to encourage consistency among common channels and distinctiveness among specific ones, and the latter disentangles common and specific representations by integrating them into a unified contrastive objective. Extensive experiments on a multicenter SRUS dataset demonstrate that the proposed dual-branch network with CW-JDRL achieves superior performance over the compared algorithms and maintains robust generalizability to external data. It suggests not only the effectiveness of SRUS for diagnosis of breast cancer, but also the potential of the proposed CAD model in clinical practice. The codes are publicly available at https://github.com/Zyh-AIUltra/CW-JDRL.