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An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning

Author

Listed:
  • Chi-Long Chen

    (Taipei Medical University
    Taipei Medical University Hospital
    Taipei Medical University)

  • Chi-Chung Chen

    (aetherAI Co., Ltd.)

  • Wei-Hsiang Yu

    (aetherAI Co., Ltd.)

  • Szu-Hua Chen

    (aetherAI Co., Ltd.)

  • Yu-Chan Chang

    (National Yang-Ming University)

  • Tai-I Hsu

    (Academia Sinica)

  • Michael Hsiao

    (Academia Sinica)

  • Chao-Yuan Yeh

    (aetherAI Co., Ltd.)

  • Cheng-Yu Chen

    (Taipei Medical University
    Taipei Medical University Hospital)

Abstract

Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole-slide images (WSIs). Most studies have employed patch-based methods, which often require detailed annotation of image patches. This typically involves laborious free-hand contouring on WSIs. To alleviate the burden of such contouring and obtain benefits from scaling up training with numerous WSIs, we develop a method for training neural networks on entire WSIs using only slide-level diagnoses. Our method leverages the unified memory mechanism to overcome the memory constraint of compute accelerators. Experiments conducted on a data set of 9662 lung cancer WSIs reveal that the proposed method achieves areas under the receiver operating characteristic curve of 0.9594 and 0.9414 for adenocarcinoma and squamous cell carcinoma classification on the testing set, respectively. Furthermore, the method demonstrates higher classification performance than multiple-instance learning as well as strong localization results for small lesions through class activation mapping.

Suggested Citation

  • Chi-Long Chen & Chi-Chung Chen & Wei-Hsiang Yu & Szu-Hua Chen & Yu-Chan Chang & Tai-I Hsu & Michael Hsiao & Chao-Yuan Yeh & Cheng-Yu Chen, 2021. "An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21467-y
    DOI: 10.1038/s41467-021-21467-y
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    Cited by:

    1. Xinke Zhang & Zihan Zhao & Ruixuan Wang & Haohua Chen & Xueyi Zheng & Lili Liu & Lilong Lan & Peng Li & Shuyang Wu & Qinghua Cao & Rongzhen Luo & Wanming Hu & Shanshan lyu & Zhengyu Zhang & Dan Xie & , 2024. "A multicenter proof-of-concept study on deep learning-based intraoperative discrimination of primary central nervous system lymphoma," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    2. Shih-Chiang Huang & Chi-Chung Chen & Jui Lan & Tsan-Yu Hsieh & Huei-Chieh Chuang & Meng-Yao Chien & Tao-Sheng Ou & Kuang-Hua Chen & Ren-Chin Wu & Yu-Jen Liu & Chi-Tung Cheng & Yu-Jen Huang & Liang-Wei, 2022. "Deep neural network trained on gigapixel images improves lymph node metastasis detection in clinical settings," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    3. Adalberto Claudio Quiros & Nicolas Coudray & Anna Yeaton & Xinyu Yang & Bojing Liu & Hortense Le & Luis Chiriboga & Afreen Karimkhan & Navneet Narula & David A. Moore & Christopher Y. Park & Harvey Pa, 2024. "Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides," Nature Communications, Nature, vol. 15(1), pages 1-24, December.

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