Meeting Banner
Abstract #4195

Jointly grading and microvascular invasion predicting of hepatocellular carcinoma based on multi-task deep learning

Yanyan Xie1, Lijuan Zhang2, Guangyi Wang3, and Wu Zhou1
1School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China, 2Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 3Department of Radiology, Guangdong General Hospital, Guangzhou, China


The pathological grade and microvascular invasion (MVI) of hepatocellular carcinoma (HCC) are two key factors related to the patient's prognosis. Previous studies usually predict these two factors separately based on medical images. In this study, we propose an end-to-end multi-task deep learning network to simultaneously predict the MVI and grading information. Specifically, we are the first to demonstrate that these two tasks are related and can promote each other in the framework of multi-task deep learning. Experimental results of HCC in Contrast-enhanced MR demonstrate the effectiveness of the proposed method, outperforming the single task learning.

This abstract and the presentation materials are available to members only; a login is required.

Join Here