Abstract
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.
Abstract (translated)
图像质量、光线条件和患者 demographic差异对从彩色 fundus 摄影中自动斜视眼检测造成了挑战。为了解决这个问题，我们提出了 Brighteye 方法，该方法基于 Vision Transformer，用于斜视眼检测和斜视眼特征分类。Brighteye 使用自注意力机制在大型 fundus 图像中的像素之间学习长距离关系。在将图像输入 Brighteye 之前，使用 YOLOv8 局部化视网膜，并裁剪围绕视网膜中心周围的区域以确保与临床实践保持对齐。视网膜检测提高了对 95% 特异性率的斜视眼检测的灵敏度，从 79.20% 提高到了 85.70%，以及对斜视眼特征分类的 Hamming 距离从 0.2470 提高到了 0.1250。在人工智能斜视眼筛查（JustRAIGS）挑战的发育阶段，Brighteye 的整体成果获得了第五名，共 226 篇论文。
URL
https://arxiv.org/abs/2405.00857
