Recently anomaly detection has drawn much attention in diagnosing ocular diseases. Most existing anomaly detection research in fundus images has relatively large anomaly scores in the salient retinal structures, such as blood vessels, optical cups and discs. In this paper, we propose a Region and Spatial Aware Anomaly Detection (ReSAD) method for fundus images, which obtains local region and long-range spatial information to reduce the false positives in the normal structure. ReSAD transfers a pre-trained model to extract the features of normal fundus images and applies the Region-and-Spatial-Aware feature Combination module (ReSC) for pixel-level features to build a memory bank. In the testing phase, ReSAD uses the memory bank to determine out-of-distribution samples as abnormalities. Our method significantly outperforms the existing anomaly detection methods for fundus images on two publicly benchmark datasets.

翻译：近年来，异常检测在眼科疾病诊断中备受关注。现有针对眼底图像的异常检测研究往往在显著视网膜结构（如血管、视杯和视盘）中产生较大的异常分数。本文提出一种基于区域和空间感知的眼底图像异常检测方法（ReSAD），该方法通过获取局部区域与长距离空间信息来减少正常结构中的假阳性。ReSAD迁移预训练模型提取正常眼底图像特征，并利用区域-空间感知特征组合模块（ReSC）对像素级特征进行建模以构建记忆库。在测试阶段，ReSAD通过记忆库判定分布外样本为异常。在两个公开基准数据集上，本方法显著优于现有眼底图像异常检测方法。