An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning
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DOI: 10.1038/s41467-021-21467-y
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Cited by:
- 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.
- 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.
- 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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