ReSynthDetect: A Fundus Anomaly Detection Network with Reconstruction and Synthetic Features
Jingqi Niu (Shanghai Jiaotong University),* Qinji Yu (Shanghai Jiao Tong University), Shiwen Dong (Shanghai Jiao Tong University), Zilong Wang (Voxelcloud), Kang Dang (Voxelcloud Inc), xiaowei ding (Shanghai Jiao Tong University)
The 34th British Machine Vision Conference
Abstract
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.Video
Citation
@inproceedings{Niu_2023_BMVC,
author = {Jingqi Niu and Qinji Yu and Shiwen Dong and Zilong Wang and Kang Dang and xiaowei ding},
title = {ReSynthDetect: A Fundus Anomaly Detection Network with Reconstruction and Synthetic Features},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://papers.bmvc2023.org/0099.pdf}
}
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