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.

利用视觉变换器的 Brighteye 方法进行青光眼检测，其中光盘检测提高了对青光眼检测的敏感性和对青光性特征分类的准确性。