  Detecting anomalies in fundus images through unsupervised methods is a
challenging task due to the similarity between normal and abnormal tissues, as
well as their indistinct boundaries. The current methods have limitations in
accurately detecting subtle anomalies while avoiding false positives. To
address these challenges, we propose the ReSynthDetect network which utilizes a
reconstruction network for modeling normal images, and an anomaly generator
that produces synthetic anomalies consistent with the appearance of fundus
images. By combining the features of consistent anomaly generation and image
reconstruction, our method is suited for detecting fundus abnormalities. The
proposed approach has been extensively tested on benchmark datasets such as
EyeQ and IDRiD, demonstrating state-of-the-art performance in both image-level
and pixel-level anomaly detection. Our experiments indicate a substantial 9%
improvement in AUROC on EyeQ and a significant 17.1% improvement in AUPR on
IDRiD.
