  Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of
chronic liver diseases (CLD) which can progress to liver cancer. The severity
and treatment of NAFLD is determined by NAFLD Activity Scores (NAS)and liver
fibrosis stage, which are usually obtained from liver biopsy. However, biopsy
is invasive in nature and involves risk of procedural complications. Current
methods to predict the fibrosis and NAS scores from noninvasive CT images rely
heavily on either a large annotated dataset or transfer learning using
pretrained networks. However, the availability of a large annotated dataset
cannot be always ensured andthere can be domain shifts when using transfer
learning. In this work, we propose a self-supervised learning method to address
both problems. As the NAFLD causes changes in the liver texture, we also
propose to use texture encoded inputs to improve the performance of the model.
Given a relatively small dataset with 30 patients, we employ a self-supervised
network which achieves better performance than a network trained via transfer
learning. The code is publicly available at
https://github.com/ananyajana/fibrosis_code.
