Multi-View disentanglement-based bidirectional generalized distillation for diagnosis of liver cancers with ultrasound images

B-mode ultrasound (BUS) mainly reflects the tissue structural, morphological, and echo characteristics of liver tumors, and contrast-enhanced ultrasound (CEUS) offers supplementary information on the dynamic blood perfusion pattern to promote diagnostic accuracy. Transfer learning (TL) is capable of improving the performance of BUS-based computer-aided diagnosis (CAD) for liver cancer by transferring information from CEUS. However, most multi-view TL algorithms cannot fully capture the view-common together with the view-unique information of three CEUS phase images in the source domain to further promote knowledge transfer. To this end, a multi-view disentanglement-based bidirectional generalized distillation (MD-BGD) algorithm is proposed to explore and learn more potential knowledge from three typical CEUS phase images for multi-view transfer. MD-BGD consists of the multi-view feature disentanglement module and the bidirectional distillation module. The former explores more potential and transferable privileged information by disentangling three CEUS phase image features in the source domain into view-common and view-unique components. The latter develops a bidirectional generalized distillation algorithm to enhance the multi-view knowledge transfer between the source and the target domains, guided by shared labels. Therefore, the BUS-based CAD model is significantly improved by our proposed MD-BGD. MD-BGD is evaluated on the bi-modal ultrasound imaging dataset. It gains the best results of 90.75±2.20 %, 89.50±3.49 %, and 91.89±3.78 %, on accuracy, sensitivity, and specificity, respectively. These results indicate the effectiveness of MD-BGD in the diagnosis of liver cancer.