  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.
