Deep Learning in Liver Biopsies using Convolutional Neural Networks

Abstract

Nonalcoholic fatty liver disease (NAFLD) presents a wide range of pathological conditions, varying from nonalcoholic steatohepatitis (NASH) to cirrhosis and hepatocellular carcinoma (HCC). Their prevalence is characterized by increased fat accumulation and hepatocellular ballooning. They have become a cause of concern among physicians and engineers, as significant implications tend to occur regarding their accurate diagnosis and treatment. Although magnetic resonance, ultrasonography and other noninvasive methods can reveal the presence of NAFLD, image quantitative interpretation through histology has become the gold standard in clinical examinations. The proposed work introduces a fully automated diagnostic tool, taking into account the high discrimination capability of histological findings in liver biopsy images. The developed methodology is based on deep supervised learning and image analysis techniques, with the determination of an efficient convolutional neural network (CNN) architecture, performing eventually a classification accuracy of 95%.