Multi-input dense convolutional network for classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma. (February 2023) - Zhang, Xuepeng; Jia, Ningyang; Wang, Yuanjun
Highlights: An automatic 3D convolutional neural network is proposed for HCC & ICC classification. Arterial and Venous sub-network are validated together with proposed MIDC-NET. Effect of feature fusion is helpful to classification performance. Abstract: Primary liver cancer is one of the leading causes of cancer deaths worldwide. The most common types of primary liver cancer are hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). Influencing factors and treatment of HCC and ICC are different in clinical. However, due to common radiographic features of them, differentiating the two types of primary liver cancer is still very challenging. We aim to propose a method based on deep learning for the classification of HCC and ICC. In the first step, we adopt a modified U-Net to segment the liver cancer lesions from preprocessed enhanced CT images and we take the segmentation regions. And then, we propose a multi-input dense convolutional network (MIDC-net) to classify hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Considering the spatial characteristics of medical images, we use a three-dimensional convolutional network to classify them. Meanwhile, in order to learn the characteristic differences of multi-stage images, arterial and venous images were used as the input of MIDC-net. The experiment result shows that the ROC curve of the method on the test data is over 0.96, and the accuracy is over 0.91.