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Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images

Author

Listed:
  • Gang Yu

    (School of Basic Medical Science, Central South University)

  • Kai Sun

    (School of Basic Medical Science, Central South University)

  • Chao Xu

    (University of Oklahoma Health Sciences Center)

  • Xing-Hua Shi

    (College of Science and Technology, Temple University)

  • Chong Wu

    (Florida State University)

  • Ting Xie

    (School of Basic Medical Science, Central South University)

  • Run-Qi Meng

    (Electronic Information Science and Technology, School of Physics and Electronics, Central South University)

  • Xiang-He Meng

    (Center for System Biology, Data Sciences and Reproductive Health, School of Basic Medical Science, Central South University)

  • Kuan-Song Wang

    (Xiangya Hospital, School of Basic Medical Science, Central South University)

  • Hong-Mei Xiao

    (Center for System Biology, Data Sciences and Reproductive Health, School of Basic Medical Science, Central South University)

  • Hong-Wen Deng

    (Center for System Biology, Data Sciences and Reproductive Health, School of Basic Medical Science, Central South University
    Deming Department of Medicine, Tulane Center of Biomedical Informatics and Genomics, Tulane University School of Medicine)

Abstract

Machine-assisted pathological recognition has been focused on supervised learning (SL) that suffers from a significant annotation bottleneck. We propose a semi-supervised learning (SSL) method based on the mean teacher architecture using 13,111 whole slide images of colorectal cancer from 8803 subjects from 13 independent centers. SSL (~3150 labeled, ~40,950 unlabeled; ~6300 labeled, ~37,800 unlabeled patches) performs significantly better than the SL. No significant difference is found between SSL (~6300 labeled, ~37,800 unlabeled) and SL (~44,100 labeled) at patch-level diagnoses (area under the curve (AUC): 0.980 ± 0.014 vs. 0.987 ± 0.008, P value = 0.134) and patient-level diagnoses (AUC: 0.974 ± 0.013 vs. 0.980 ± 0.010, P value = 0.117), which is close to human pathologists (average AUC: 0.969). The evaluation on 15,000 lung and 294,912 lymph node images also confirm SSL can achieve similar performance as that of SL with massive annotations. SSL dramatically reduces the annotations, which has great potential to effectively build expert-level pathological artificial intelligence platforms in practice.

Suggested Citation

  • Gang Yu & Kai Sun & Chao Xu & Xing-Hua Shi & Chong Wu & Ting Xie & Run-Qi Meng & Xiang-He Meng & Kuan-Song Wang & Hong-Mei Xiao & Hong-Wen Deng, 2021. "Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26643-8
    DOI: 10.1038/s41467-021-26643-8
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    References listed on IDEAS

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    1. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
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    Cited by:

    1. Ana Stanojevic & Stanisław Woźniak & Guillaume Bellec & Giovanni Cherubini & Angeliki Pantazi & Wulfram Gerstner, 2024. "High-performance deep spiking neural networks with 0.3 spikes per neuron," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    2. Kang-Bo Huang & Cheng-Peng Gui & Yun-Ze Xu & Xue-Song Li & Hong-Wei Zhao & Jia-Zheng Cao & Yu-Hang Chen & Yi-Hui Pan & Bing Liao & Yun Cao & Xin-Ke Zhang & Hui Han & Fang-Jian Zhou & Ran-Yi Liu & Wen-, 2024. "A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    3. Pei-Chen Tsai & Tsung-Hua Lee & Kun-Chi Kuo & Fang-Yi Su & Tsung-Lu Michael Lee & Eliana Marostica & Tomotaka Ugai & Melissa Zhao & Mai Chan Lau & Juha P. Väyrynen & Marios Giannakis & Yasutoshi Takas, 2023. "Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    4. Darui Jin & Shangying Liang & Artem Shmatko & Alexander Arnold & David Horst & Thomas G. P. Grünewald & Moritz Gerstung & Xiangzhi Bai, 2024. "Teacher-student collaborated multiple instance learning for pan-cancer PDL1 expression prediction from histopathology slides," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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