Preliminary study of liver cirrhosis staging and classification using deep metric learning network

Abstract

Progression staging and classification of liver cirrhosis plays important role in determining the accurate treatment and assessing clinical efficacy. Currently, liver biopsy is the gold standard method for liver cirrhosis staging via sampling the real liver tissue, which imposes heavy burden on the patient. To alleviate the heavy burden on patients, recent research pays extensive attention on non-invasive methods such as blood tests and medical images for liver cirrhosis diagnosis. In this paper, we investigate a non-invasive progression staging method of liver cirrhosis using MRI images and deep learning methods. This study exploits a novel module (dubbed as AFM module) consisting of additive angular margin and fisher margin, and integrates it deep learning network to maximize the cirrhosis stage separability. Experiments on the MRI images provided by Shandong University, which includes three progression stages of liver cirrhosis: early, middle and last stages, validate that the performance gain with the integration of the proposed AFM module are from 3% to 7% compared to the baseline models: VGG16, ResNet18, and ResNet50.