Uncertainty-inspired open set learning for retinal anomaly identification

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 () and Huazhu Fu ()
Additional contact information
Meng Wang: Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)
Tian Lin: Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong
Lianyu Wang: Nanjing University of Aeronautics and Astronautics
Aidi Lin: Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong
Ke Zou: National Key Laboratory of Fundamental Science on Synthetic Vision and the College of Computer Science, Sichuan University
Xinxing Xu: Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)
Yi Zhou: Soochow University
Yuanyuan Peng: Anhui Medical University
Qingquan Meng: Soochow University
Yiming Qian: Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)
Guoyao Deng: National Key Laboratory of Fundamental Science on Synthetic Vision and the College of Computer Science, Sichuan University
Zhiqun Wu: Longchuan People’s Hospital
Junhong Chen: Puning People’s Hospital
Jianhong Lin: Haifeng PengPai Memory Hospital
Mingzhi Zhang: Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong
Weifang Zhu: Soochow University
Changqing Zhang: College of Intelligence and Computing, Tianjin University
Daoqiang Zhang: Nanjing University of Aeronautics and Astronautics
Rick Siow Mong Goh: Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)
Yong Liu: Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)
Chi Pui Pang: Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong
Xinjian Chen: Soochow University
Haoyu Chen: Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong
Huazhu Fu: Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: Abstract Failure 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.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-023-42444-7 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42444-7

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-023-42444-7

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().