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Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes ofchronic liver diseases (CLD) which can progress to liver cancer. The severityand treatment of NAFLD is determined by NAFLD Activity Scores (NAS)and liverfibrosis stage, which are usually obtained from liver biopsy. However, biopsyis invasive in nature and involves risk of procedural complications. Currentmethods to predict the fibrosis and NAS scores from noninvasive CT images relyheavily on either a large annotated dataset or transfer learning usingpretrained networks. However, the availability of a large annotated datasetcannot be always ensured andthere can be domain shifts when using transferlearning. In this work, we propose a self-supervised learning method to addressboth problems. As the NAFLD causes changes in the liver texture, we alsopropose to use texture encoded inputs to improve the performance of the model.Given a relatively small dataset with 30 patients, we employ a self-supervisednetwork which achieves better performance than a network trained via transferlearning. The code is publicly available athttps://github.com/ananyajana/fibrosis_code.

Liver Fibrosis and NAS scoring from CT images using self-supervised learning and texture encoding