Galar - a large multi-label video capsule endoscopy dataset

We propose Galar, the most comprehensive dataset of VCE to date. Galar consists of 80 videos, culminating in 3,513,539 annotated frames covering functional, anatomical, and pathological aspects and introducing a selection of 29 distinct labels. The multisystem and multicenter VCE data from two centers in Saxony (Germany), was annotated framewise and cross-validated by five annotators. The vast scope of annotation and size of Galar make the dataset a valuable resource for the use of AI models in VCE, thereby facilitating research in diagnostic methods, patient care workflow, and the development of predictive analytics in the field.<br><br>Update2025-12: Correction of 6.csv - error on first line of table (anatomy region missing)

本研究提出Galar数据集，该数据集为目前规模最为全面的视频胶囊内镜（Video Capsule Endoscopy，VCE）数据集。Galar共包含80段视频，总计3513539帧标注帧，覆盖功能、解剖与病理三大研究维度，并设置了29种独立标注标签。该多系统、多中心VCE数据采集自德国萨克森州的两家研究中心，由5名标注人员逐帧标注并完成交叉验证。Galar庞大的标注覆盖范围与数据集体量，使其成为VCE领域人工智能模型应用的宝贵资源，进而推动该领域在诊断方法、患者护理流程以及预测分析开发等方向的研究。

2025年12月更新：修正6.csv文件——修复表格首行缺失解剖区域标注的错误

搜集汇总

背景与挑战

背景概述

Galar是一个大型多标签视频胶囊内窥镜数据集，包含80个视频和超过350万标注帧，涵盖29个功能、解剖和病理标签。该数据集由多中心合作创建，经过严格标注和验证，旨在推动AI在胶囊内窥镜领域的研究和应用。