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Qualifying Exam

Deep Learning based NAS Score and Fibrosis Stage Prediction from CT data


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Friday, December 18, 2020, 01:00pm - 03:00pm


Speaker: Ananya Jana

Location : Remote via Webex


Prof. Dimitris Metaxas (Advisor)

Prof. Vladimir Pavlovic

Prof. Sungjin Ahn

Prof. Aaron Bernstein

Event Type: Qualifying Exam

Abstract: Non-Alcoholic Fatty Liver Disease (NAFLD) is becoming increasingly prevalent in the world population. Without diagnosis at the right time, NAFLD can lead to non-alcoholic steatohepatitis (NASH) and subsequent liver damage. The diagnosis and treatment of NAFLD depend on the NAFLD activity score (NAS) and the liver fibrosis stage, which are usually evaluated from liver biopsies by pathologists. We propose a novel method to automatically predict NAS score and fibrosis stage from CT data that is non-invasive and inexpensive to obtain compared with liver biopsy. We also present a method to combine the information from CT and H\&E stained pathology data to improve the performance of NAS score and fibrosis stage prediction, when both types of data are available. However, the availability of a large annotated dataset cannot be always ensured and there can be domain shifts when using transfer learning. So, 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.


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