Expanding Training Data for Endoscopic Phenotyping of Eosinophilic Esophagitis

• URL: http://arxiv.org/abs/2502.04199v1
• Date: Thu, 06 Feb 2025 16:38:47 GMT
• Title: Expanding Training Data for Endoscopic Phenotyping of Eosinophilic Esophagitis
• Authors: Juming Xiong, Hou Xiong, Quan Liu, Ruining Deng, Regina N Tyree, Girish Hiremath, Yuankai Huo,
• Abstract summary: Eosinophilic esophagitis (EoE) is a chronic esophageal disorder marked by eosinophil-dominated inflammation. Recent advances have seen AI-assisted endoscopic imaging, guided by the EREFS system, emerge as a potential alternative to reduce reliance on invasive histologic assessments. This study seeks to improve the performance of deep learning-based EoE phenotype classification by augmenting our training data with a diverse set of images from online platforms, public datasets, and electronic textbooks.
• Score: 9.044271577557721
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• Abstract: Eosinophilic esophagitis (EoE) is a chronic esophageal disorder marked by eosinophil-dominated inflammation. Diagnosing EoE usually involves endoscopic inspection of the esophageal mucosa and obtaining esophageal biopsies for histologic confirmation. Recent advances have seen AI-assisted endoscopic imaging, guided by the EREFS system, emerge as a potential alternative to reduce reliance on invasive histological assessments. Despite these advancements, significant challenges persist due to the limited availability of data for training AI models - a common issue even in the development of AI for more prevalent diseases. This study seeks to improve the performance of deep learning-based EoE phenotype classification by augmenting our training data with a diverse set of images from online platforms, public datasets, and electronic textbooks increasing our dataset from 435 to 7050 images. We utilized the Data-efficient Image Transformer for image classification and incorporated attention map visualizations to boost interpretability. The findings show that our expanded dataset and model enhancements improved diagnostic accuracy, robustness, and comprehensive analysis, enhancing patient outcomes.

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