扩充内镜图像数据集，提升嗜酸性食管炎智能诊断准确率
Expanding Training Data for Endoscopic Phenotyping of Eosinophilic Esophagitis
- 从435张增至7050张图像，融合网络、公开数据与电子教材扩充训练集
- 模型在扩展数据上达到92.3%准确率，显著提升诊断鲁棒性
- 采用注意力可视化增强可解释性，适合临床辅助诊断场景
嗜酸性食管炎（EoE）是一种以嗜酸性粒细胞浸润为特征的慢性食管疾病，通常需通过内镜观察和组织活检确诊。近年来，基于EREFs系统的AI辅助内镜影像技术有望减少对侵入性病理检查的依赖。然而，由于训练数据稀缺，该领域仍面临重大挑战。本研究通过整合网络平台、公共数据集及电子教材中的多样化图像，将训练数据从435张扩展至7050张。采用Data-efficient Image Transformer进行分类，并引入注意力图可视化提升模型可解释性。结果表明，扩充后的数据集与模型优化显著提升了诊断准确率、鲁棒性和综合分析能力，有助于改善患者诊疗效果。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。