Brighteye：基于视觉Transformer的彩色眼底照片青光眼筛查
Brighteye: Glaucoma Screening with Color Fundus Photographs based on Vision Transformer
AI总结：
本文提出基于视觉Transformer的Brighteye方法，结合YOLOv8视盘定位与ROI裁剪，在青光眼检测和特征分类上显著提升性能，并在JustRAIGS挑战赛中排名第五。
摘要：
图像质量、光照条件和患者人口统计学特征的差异给基于彩色眼底照相的自动化青光眼检测带来了挑战。本文提出了一种基于视觉Transformer（Vision Transformer）的方法Brighteye，用于青光眼检测和青光眼特征分类。Brighteye利用自注意力机制学习大幅眼底图像中像素之间的长程关系。在输入Brighteye之前，先使用YOLOv8对视盘进行定位，并围绕视盘中心裁剪感兴趣区域（ROI），以确保与临床实践保持一致。视盘检测将青光眼检测在95%特异度下的灵敏度从79.20%提高到85.70%，并将青光眼特征分类的汉明距离从0.2470降低到0.1250。在Justified Referral in AI Glaucoma Screening（JustRAIGS）挑战赛的开发阶段，该方法在226个参赛作品中总体排名第五。
英文摘要：
Differences in image quality, lighting conditions, and patient demographics pose challenges to automated glaucoma detection from color fundus photography. Brighteye, a method based on Vision Transformer, is proposed for glaucoma detection and glaucomatous feature classification. Brighteye learns long-range relationships among pixels within large fundus images using a self-attention mechanism. Prior to being input into Brighteye, the optic disc is localized using YOLOv8, and the region of interest (ROI) around the disc center is cropped to ensure alignment with clinical practice. Optic disc detection improves the sensitivity at 95% specificity from 79.20% to 85.70% for glaucoma detection and the Hamming distance from 0.2470 to 0.1250 for glaucomatous feature classification. In the developmental stage of the Justified Referral in AI Glaucoma Screening (JustRAIGS) challenge, the overall outcome secured the fifth position out of 226 entries.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
