Automated Classification of Acute Rejection from Endomyocardial Biopsies

Abstract

Heart transplant rejection must be quickly and accurately identified to optimize anti-rejection therapies and prevent organ loss. Expert evaluation of endomyocardial biopsies is labor-intensive, and prone to human bias, and suffers from low inter-rater agreement. Additionally, the increased utility of digital pathology for biopsy examination has exacerbated the need for additional image quality control. To meet these challenges, we developed a novel transplant rejection detection pipeline which automatically identifies histology slides in need of rescanning and highlights biopsy regions showing potential signs of rejection. Our system leverages a fast and effective automated patch-level quality filter as well as state-of-the-art feature extraction techniques to provide quality whole-slide level labeling of early rejection signs. We successfully identified digital pathology images with poor image quality and leveraged this quality gain to improve our novel weakly-supervised learning model leading to significant transplant rejection classification performance of AUC: 70.12 (±20.74) %.