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Background. Eosinophilic esophagitis (EoE) is an allergic inflammatorycondition of the esophagus associated with elevated numbers of eosinophils.Disease diagnosis and monitoring requires determining the concentration ofeosinophils in esophageal biopsies, a time-consuming, tedious and somewhatsubjective task currently performed by pathologists. Methods. Herein, we aimedto use machine learning to identify, quantitate and diagnose EoE. We labeledmore than 100M pixels of 4345 images obtained by scanning whole slides ofH&E-stained sections of esophageal biopsies derived from 23 EoE patients. Weused this dataset to train a multi-label segmentation deep network. To validatethe network, we examined a replication cohort of 1089 whole slide images from419 patients derived from multiple institutions. Findings. PECNet segmentedboth intact and not-intact eosinophils with a mean intersection over union(mIoU) of 0.93. This segmentation was able to quantitate intact eosinophilswith a mean absolute error of 0.611 eosinophils and classify EoE diseaseactivity with an accuracy of 98.5%. Using whole slide images from thevalidation cohort, PECNet achieved an accuracy of 94.8%, sensitivity of 94.3%,and specificity of 95.14% in reporting EoE disease activity. Interpretation. Wehave developed a deep learning multi-label semantic segmentation network thatsuccessfully addresses two of the main challenges in EoE diagnostics anddigital pathology, the need to detect several types of small featuressimultaneously and the ability to analyze whole slides efficiently. Our resultspave the way for an automated diagnosis of EoE and can be utilized for otherconditions with similar challenges.

PECNet: A Deep Multi-Label Segmentation Network for Eosinophilic Esophagitis Biopsy Diagnostics