Diagnosis of hepatocellular carcinoma using deep network with multi-view enhanced patterns mined in contrast-enhanced ultrasound data

Hepatocellular carcinoma, representing the most frequent primary liver cancer, is a common cancer disease that is the fourth leading cause of cancer-related mortality worldwide. In comparison, non-hepatocellular carcinoma liver cancers often present different prognoses and require distinct management which makes the accurate discrimination between hepatocellular carcinoma and non-hepatocellular carcinoma malignant lesions in contrast-enhanced ultrasound data critical for precise intervention. However, different types of liver cancers have similar enhanced patterns against the perfusion stages that raise the difficulty in the classification of hepatocellular carcinoma with the other liver cancers, especially when the contrast-enhanced ultrasound data is collected from different imaging machines. To this end, this paper innovatively proposes to extract perfusion features from a multi-view learning procedure for obtaining the inherent distinguishing features among liver cancers, leading to a more precise deep model in differentiating the hepatocellular carcinoma from other malignant cases. In particular, the proposed network consists of two novel structures for learning the correlation information among the different views to enhance the robustness of the features and fuse them by reducing redundant information. The proposed method is verified on a multi-source dataset collected from 1241 participants and achieves an AUC value of 89% for classification performance. The experimental results demonstrate the effectiveness of the proposed method for the diagnosis of hepatocellular carcinoma with a multi-source contrast-enhanced ultrasound dataset and might provide an effective assistant for clinical radiologists in liver cancer differentiation.