SCCNN: A Diagnosis Method for Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Based on Siamese Cross Contrast Neural Network

Abstract

This paper proposes a novel siamese cross contrast neural network (SCCNN) to classify the hepatocellular carcinoma (HCC) and the intrahepatic cholangiocarcinoma (ICC) on computed tomography (CT) images. This method is inspired from cross contrast neural networks (CCNN) which is based on tailored CNN and information based similarity(IBS) theory. A new IBS-based measurement named as discriminative IBS(DisIBS) is designed for SCCNN. SCCNN is composed of two main parts including siamese feature extractors with DisIBS operator and MLP classifiers. Siamese networks extract features with DisIBS calculated by DisIBS operator as metric at the top. MLP classifiers are connected with but gradient-stop to feature extractors deriving classification results. We assign different loss functions with different parts to make better practice, specially DisIBS-based loss for feature extractors and softmax-based for MLP classifiers. SCCNN preserves the advantages of CCNN that can fit the insufficient medical images and small lesions. Furthermore, it extends CCNN with the siamese mechanism and gradient-stop MLP classifiers to accept the random inputs and predict like traditional CNN. To present the effectiveness of SCCNN empirically, we apply this method on a 234-person (157/77 for train/test) dataset and achieve better results than other classic CNN and CCNN methods. We try different base models of siamese structures and display prediction accuracy in two levels (slice/patient). The highest slice/patient accuracy which we have achieved on three-categories classification (HCC/ICC/Normal) is 90.22%/94.92% and the accuracy rises to 94.17%/97.44% on binary classification(HCC/ICC).