Eosinophilic esophagitis (EoE) is an immune-mediated, clinicopathologic disease of the esophagus. EoE is histologically characterized by the accretion of eosinophils in the esophageal epithelium. The current practice involving manual identification of the small-scale histologic features of EoE relative to the size of the esophageal biopsies can be burdensome and prone to interpreter errors. The existing automatic, computer-assisted EoE identification approaches are typically designed as a train-from-scratch setting, which is prone to overfitting. In this study, we propose to use transfer deep-learning via both the ImageNet pre-trained ResNet50 as well as the more recent Big Transfer (BiT) model to achieve automated EoE feature identification on whole slide images. As opposed to existing deep-learning-based approaches that typically focus on a single pathological phenotype, our study investigates five EoE-relevant histologic features including basal zone hyperplasia, dilated intercellular spaces, eosinophils, lamina propria fibrosis, and normal lamina propria simultaneously. From the results, the model achieved a promising testing balanced accuracy of 61.9%, which is better than that of its trained-from-scratch counterparts.
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