ReSynthDetect: A Fundus Anomaly Detection Network with Reconstruction and Synthetic Features
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 16 of its 32 references that are in the map. First posted on arXiv 27 Dec 2023.
Builds on
16 of 32 references.
References · 16
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4 more
- 2019Evaluation of Retinal Image Quality Assessment Networks in Different Color-spacescited by 57
- 2020Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Imagescited by 52
- 2021Fine-tuning Pretrained Multilingual BERT Model for Indonesian Aspect-based Sentiment Analysiscited by 4
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2 papers.