Correlation-Adaptive Multi-view CEUS Fusion for Liver Cancer Diagnosis

Peng Wan, Shukang Zhang, Wei Shao, Junyong Zhao, Yinkai Yang, Wentao Kong, Haiyan Xue, Daoqiang Zhang

Abstract: Dual-screen contrast-enhanced ultrasound (CEUS) has been the first-line imaging techniques for the differential diagnosis of primary liver cancer (PLC), since the imaging of tumor micro-circulation perfusion as well as anatomic features of B-mode ultrasound (BUS) view. Although previous multi-view learning methods have shown their potential to boost diagnostic efficacy, correlation variances of different views among subjects are largely underestimated, arising from the varying imaging quality of different views and the presence of valuable findings or not. In this paper, we propose a correlation-adaptive multi-view fusion method (CAMVF) for dual-screen CEUS based PLC diagnosis. Towards a reliable fusion of multi-view CEUS findings (i.e., BUS, CEUS and its parametric imaging), our method dynamically assesses the correlation of each view based on the prediction confidence itself and prediction consistency among views. Specifically, we first obtain the confidence of each view with evidence-based uncertainty estimation, then divide them into credible and incredible views based on cross-view consistency, and finally ensemble views with weights adaptive to their credibility. In this retrospective study, we collected CEUS imaging from 238 liver cancer patients in total, and our method achieves the superior diagnostic accuracy and specificity of 88.33% and 92.48%, respectively, demonstrating its efficacy for PLC differential diagnosis. Our code is available at https://github.com/shukangzh/CAMVF.