A deep learning method for counting white blood cells in bone marrow images.

Journal: BMC bioinformatics
Published Date: Nov 8, 2021

Abstract

BACKGROUND: Differentiating and counting various types of white blood cells (WBC) in bone marrow smears allows the detection of infection, anemia, and leukemia or analysis of a process of treatment. However, manually locating, identifying, and counting the different classes of WBC is time-consuming and fatiguing. Classification and counting accuracy depends on the capability and experience of operators.

Authors

  • Da Wang
    Department of Colorectal Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China; Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Zhejiang Province, China; The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Maxwell Hwang
    Department of Colorectal Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
  • Wei-Cheng Jiang
    Department of Electrical Engineering, Tunghai University, Taichung, Taiwan, China.
  • Kefeng Ding
    Department of Surgical Oncology, The Second Affiliated Hospital of Zhejiang University Medical School, Hangzhou, China.
  • Hsiao Chien Chang
    Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan, China.
  • Kao-Shing Hwang
    Department of Healthcare Administration and Medical Informatics, 38023Kaohsiung Medical University, Kaohsiung 807, Taiwan.

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