Uncertainty-inspired open set learning for retinal anomaly identification

Uncertainty-inspired open set learning for retinal anomaly identification

Nature Communications

10.1038/s41467-023-42444-7

http://dx.doi.org/10.1038/s41467-023-42444-7

Meng Wang, Tian Lin, Lianyu Wang, Aidi Lin, Ke Zou, Xinxing Xu, Yi Zhou, Yuanyuan Peng, Qingquan Meng, Yiming Qian, Guoyao Deng, Zhiqun Wu, Junhong Chen, Jianhong Lin, Mingzhi Zhang, Weifang Zhu, Changqing Zhang, Daoqiang Zhang, Rick Siow Mong Goh, Yong Liu, Chi Pui Pang, Xinjian Chen, Haoyu Chen, Huazhu Fu

24 October 2023

Wang, M., Lin, T., Wang, L., Lin, A., Zou, K., Xu, X., Zhou, Y., Peng, Y., Meng, Q., Qian, Y., Deng, G., Wu, Z., Chen, J., Lin, J., Zhang, M., Zhu, W., Zhang, C., Zhang, D., Goh, R. S. M., … Fu, H. (2023). Uncertainty-inspired open set learning for retinal anomaly identification. Nature Communications, 14(1). https://doi.org/10.1038/s41467-023-42444-7

AbstractFailure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies. We establish an uncertainty-inspired open set (UIOS) model, which is trained with fundus images of 9 retinal conditions. Besides assessing the probability of each category, UIOS also calculates an uncertainty score to express its confidence. Our UIOS model with thresholding strategy achieves an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set, external target categories (TC)-JSIEC dataset and TC-unseen testing set, respectively, compared to the F1 score of 92.20%, 80.69% and 64.74% by the standard AI model. Furthermore, UIOS correctly predicts high uncertainty scores, which would prompt the need for a manual check in the datasets of non-target categories retinal diseases, low-quality fundus images, and non-fundus images. UIOS provides a robust method for real-world screening of retinal anomalies.

Attribution 4.0 International (CC BY 4.0)

This research / project is supported by the A*STAR - Career Development Fund
Grant Reference no. : C222812010

This research / project is supported by the A*STAR - Central Research Fund
Grant Reference no. : NA

This research / project is supported by the A*STAR - AME Programmatic
Grant Reference no. : A20H4b0141

https://oar.a-star.edu.sg/communities-collections/articles/19862

2041-1723

Institute of High Performance Computing 

https://doi.org/10.1038/s41467-023-42444-7