Background: Early-stage diagnosis and treatment can improve survival rates of liver cancer
patients. Dynamic contrast-enhanced MRI provides the most comprehensive information for …

Objectives To develop and validate a proof-of-concept convolutional neural network (CNN)–
based deep learning system (DLS) that classifies common hepatic lesions on multi-phasic …

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors and a
leading cause of cancer-related death worldwide. We propose a fully automated deep …

Objectives To develop a proof-of-concept “interpretable” deep learning prototype that
justifies aspects of its predictions from a pre-trained hepatic lesion classifier. Methods A …

Objectives To train a deep learning model to differentiate between pathologically proven
hepatocellular carcinoma (HCC) and non-HCC lesions including lesions with atypical …

Objective Radiomic and deep learning studies based on magnetic resonance imaging (MRI)
of liver tumor are gradually increasing. Manual segmentation of normal hepatic tissue and …

Background Liver cancer remains the leading cause of cancer death globally, and the
treatment strategies are distinct for each type of malignant hepatic tumors. However, the …

Abstract Purpose Liver Imaging Reporting and Data System (LI-RADS) uses multiphasic
contrast-enhanced imaging for hepatocellular carcinoma (HCC) diagnosis. The goal of this …

Purpose Liver cancer is one of the most common types of cancers in Asia with a high
mortality rate. A common method for liver cancer diagnosis is the manual examination of …

Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past
decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of …