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Annotation-efficient deep learning for automatic medical image segmentation

Author

Listed:
  • Shanshan Wang

    (Chinese Academy of Sciences
    Peng Cheng Laboratory
    Pazhou Laboratory)

  • Cheng Li

    (Chinese Academy of Sciences)

  • Rongpin Wang

    (Guizhou Provincial People’s Hospital)

  • Zaiyi Liu

    (Guangdong General Hospital, Guangdong Academy of Medical Sciences)

  • Meiyun Wang

    (Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University)

  • Hongna Tan

    (Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University)

  • Yaping Wu

    (Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University)

  • Xinfeng Liu

    (Guizhou Provincial People’s Hospital)

  • Hui Sun

    (Chinese Academy of Sciences)

  • Rui Yang

    (Renmin Hospital of Wuhan University)

  • Xin Liu

    (Chinese Academy of Sciences)

  • Jie Chen

    (Peng Cheng Laboratory
    Shenzhen Graduate School, Peking University)

  • Huihui Zhou

    (Chinese Academy of Sciences)

  • Ismail Ayed

    (ETS Montreal)

  • Hairong Zheng

    (Chinese Academy of Sciences)

Abstract

Automatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, we introduce Annotation-effIcient Deep lEarning (AIDE), an open-source framework to handle imperfect training datasets. Methodological analyses and empirical evaluations are conducted, and we demonstrate that AIDE surpasses conventional fully-supervised models by presenting better performance on open datasets possessing scarce or noisy annotations. We further test AIDE in a real-life case study for breast tumor segmentation. Three datasets containing 11,852 breast images from three medical centers are employed, and AIDE, utilizing 10% training annotations, consistently produces segmentation maps comparable to those generated by fully-supervised counterparts or provided by independent radiologists. The 10-fold enhanced efficiency in utilizing expert labels has the potential to promote a wide range of biomedical applications.

Suggested Citation

  • Shanshan Wang & Cheng Li & Rongpin Wang & Zaiyi Liu & Meiyun Wang & Hongna Tan & Yaping Wu & Xinfeng Liu & Hui Sun & Rui Yang & Xin Liu & Jie Chen & Huihui Zhou & Ismail Ayed & Hairong Zheng, 2021. "Annotation-efficient deep learning for automatic medical image segmentation," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26216-9
    DOI: 10.1038/s41467-021-26216-9
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    References listed on IDEAS

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    1. Sarah Webb, 2018. "Deep learning for biology," Nature, Nature, vol. 554(7693), pages 555-557, February.
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    Cited by:

    1. Mi, Yunlong & Wang, Zongrun & Quan, Pei & Shi, Yong, 2024. "A semi-supervised concept-cognitive computing system for dynamic classification decision making with limited feedback information," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1123-1138.

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