用CNN模型自动识别10类胃肠道异常，提升胶囊内镜诊断效率
Optimizing Gastrointestinal Diagnostics: A CNN-Based Model for VCE Image Classification
- 基于CNN设计专用架构，实现10类胃肠病变多分类
- 支持从胶囊内镜图像中自动识别出血、溃疡、寄生虫等异常
- 为无厂商依赖的AI辅助诊断提供可复现方案，适合临床研发参考
近年来，随着高精度视频胶囊内镜（VCE）技术的发展，胃肠道（GI）疾病诊断取得了显著进展，实现了消化道的非侵入式观察。MisaHub Capsule Vision Challenge旨在推动无需依赖设备厂商的人工智能模型开发，实现对VCE图像中GI异常的自主分类。本文提出一种专用于多类别分类的卷积神经网络（CNN）架构，可识别十类胃肠道病理状态，包括血管扩张、出血、糜烂、红斑、异物、淋巴管扩张、息肉、溃疡、寄生虫以及正常状态。
原文摘要 · Abstract (English)
In recent years, the diagnosis of gastrointestinal (GI) diseases has advanced greatly with the advent of high-tech video capsule endoscopy (VCE) technology, which allows for non-invasive observation of the digestive system. The MisaHub Capsule Vision Challenge encourages the development of vendor-independent artificial intelligence models that can autonomously classify GI anomalies from VCE images. This paper presents CNN architecture designed specifically for multiclass classification of ten gut pathologies, including angioectasia, bleeding, erosion, erythema, foreign bodies, lymphangiectasia, polyps, ulcers, and worms as well as their normal state.
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