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The Diagnosis of Chronic Myeloid Leukemia with Deep Adversarial Learning

  • Zelin Zhang
    Affiliations
    Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing, China
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  • Xianqi Huang
    Affiliations
    State Key Laboratory of Experimental Hematology, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China

    SINO-US Diagnostics Lab, Tianjin Enterprise Key Laboratory of AI-aided Hematopathology Diagnosis, Tianjin, China
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  • Qi Yan
    Affiliations
    State Key Laboratory of Experimental Hematology, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China

    SINO-US Diagnostics Lab, Tianjin Enterprise Key Laboratory of AI-aided Hematopathology Diagnosis, Tianjin, China
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  • Yani Lin
    Affiliations
    SINO-US Diagnostics Lab, Tianjin Enterprise Key Laboratory of AI-aided Hematopathology Diagnosis, Tianjin, China

    AI Diagnosis Lab, Shenzhen, China
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  • Enbin Liu
    Affiliations
    SINO-US Diagnostics Lab, Tianjin Enterprise Key Laboratory of AI-aided Hematopathology Diagnosis, Tianjin, China

    AI Diagnosis Lab, Shenzhen, China
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  • Yingchang Mi
    Affiliations
    State Key Laboratory of Experimental Hematology, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
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  • Shi Liang
    Affiliations
    Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing, China
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  • Hao Wang
    Affiliations
    National Institutes for Food and Drug Control, Beijing, China
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  • Jun Xu
    Correspondence
    Jun Xu, Ph.D., Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science & Technology, No. 219 Ningliu Rd., Jiangbei New Area, Nanjing 210044, China.
    Affiliations
    Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing, China
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  • Kun Ru
    Correspondence
    Address correspondence to Kun Ru, M.D., Ph.D., SINO-US Diagnostics Lab, Tianjin Enterprise Key Laboratory of AI-aided Hematopathology Diagnosis, Xinghua San Zhi Rd., Tianjin 300385, China.
    Affiliations
    SINO-US Diagnostics Lab, Tianjin Enterprise Key Laboratory of AI-aided Hematopathology Diagnosis, Tianjin, China

    AI Diagnosis Lab, Shenzhen, China
    Search for articles by this author
      Chronic myeloid leukemia (CML) is a clonal proliferative disorder of granulocytic lineage, with morphologic evaluation as the first step for a definite diagnosis. This study developed a conditional generative adversarial network (cGAN)–based model, CMLcGAN, to segment megakaryocytes from myeloid cells in bone marrow biopsies. After segmentation, the statistical characteristics of two types of cells were extracted and compared between patients and controls. At the segmentation phase, the CMLcGAN was evaluated on 517 images (512 × 512) which achieved a mean pixel accuracy of 95.1%, a mean intersection over union of 71.2%, and a mean Dice coefficient of 81.8%. In addition, the CMLcGAN was compared with seven other available deep learning–based segmentation models and achieved a better segmentation performance. At the clinical validation phase, a series of seven-dimensional statistical features from various cells were extracted. Using the t-test, five-dimensional features were selected as the clinical prediction feature set. Finally, the model iterated 100 times using threefold cross-validation on whole slide images (58 CML cases and 31 healthy cases), and the final best AUC was 84.93%. In conclusion, a CMLcGAN model was established for multiclass segmentation of bone marrow cells that performed better than other deep learning–based segmentation models.
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