Poster + Paper
4 April 2022 Eosinophilic esophagitis multi-label feature recognition on whole slide imaging using transfer learning
Author Affiliations +
Conference Poster
Abstract
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.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yuxuan Shi, Quan Liu, Jiachen Xu, Zuhayr Asad, Can Cui, Hernán Correa, Yash Choksi, Girish Hiremath, and Yuankai Huo "Eosinophilic esophagitis multi-label feature recognition on whole slide imaging using transfer learning", Proc. SPIE 12039, Medical Imaging 2022: Digital and Computational Pathology, 1203919 (4 April 2022); https://doi.org/10.1117/12.2611521
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KEYWORDS
Tissues

Distributed interactive simulations

Data modeling

Biopsy

Image segmentation

Binary data

Performance modeling

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