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Uncertainty-inspired open set learning for retinal anomaly identification

Author

Listed:
  • 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
    Laboratory of Brain-Machine Intelligence Technology, Ministry of Education 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
    Laboratory of Brain-Machine Intelligence Technology, Ministry of Education 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
    The Chinese University of Hong Kong)

  • Xinjian Chen

    (Soochow University
    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))

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.

Suggested Citation

  • 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 & We, 2023. "Uncertainty-inspired open set learning for retinal anomaly identification," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42444-7
    DOI: 10.1038/s41467-023-42444-7
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    References listed on IDEAS

    as
    1. Ling-Ping Cen & Jie Ji & Jian-Wei Lin & Si-Tong Ju & Hong-Jie Lin & Tai-Ping Li & Yun Wang & Jian-Feng Yang & Yu-Fen Liu & Shaoying Tan & Li Tan & Dongjie Li & Yifan Wang & Dezhi Zheng & Yongqun Xiong, 2021. "Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    2. Henrik Olsson & Kimmo Kartasalo & Nita Mulliqi & Marco Capuccini & Pekka Ruusuvuori & Hemamali Samaratunga & Brett Delahunt & Cecilia Lindskog & Emiel A. M. Janssen & Anders Blilie & Lars Egevad & Ola, 2022. "Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
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