Local and Non-local Deep Feature Fusion for Malignancy Characterization of Hepatocellular Carcinoma

Abstract

Deep feature derived from convolutional neural network (CNN) has demonstrated superior ability to characterize the biological aggressiveness of tumors, which is typically based on convolutional operations repeatedly processed within a local neighborhood. Due to the heterogeneity of lesions, such local deep feature may be insufficient to represent the aggressiveness of neoplasm. Inspired by the non-local neural networks in computer vision, the non-local deep feature may be remarkably complementary for lesion characterization. In this work, we propose a local and non-local deep feature fusion model based on common and individual feature analysis by extracting common and individual components of local and non-local deep features to characterize the biological aggressiveness of lesions. Specifically, we first design a non-local subnetwork for non-local deep feature extraction of neoplasm, and subsequently combine local and non-local deep features with a specific designed fusion subnetwork based on common and individual feature analysis. Experimental results of malignancy characterization of clinical hepatocellular carcinoma (HCC) with Contrast-enhanced MR images demonstrate several intriguing features of the proposed local and non-local deep feature fusion model as follows: (1) Non-local deep feature outperforms local deep feature for lesion characterization; (2) The fusion of local and non-local deep feature yields further improved performance of lesion characterization; (3) The fusion method of common and individual feature analysis outperforms the method of simple concatenation and the method of deep correlation model.