OCDet: A comprehensive ovarian cell detection model with channel attention on immunohistochemical and morphological pathology images.

Journal: Computers in biology and medicine
PMID: 39864335

Abstract

BACKGROUND: Ovarian cancer is among the most lethal gynecologic malignancy that threatens women's lives. Pathological diagnosis is a key tool for early detection and diagnosis of ovarian cancer, guiding treatment strategies. The evaluation of various ovarian cancer-related cells, based on morphological and immunohistochemical pathology images, is deemed an important step. Currently, the lack of a comprehensive deep learning framework for detecting various ovarian cells poses a performance bottleneck in ovarian cancer pathological diagnosis.

Authors

  • Jing Peng
  • Qiming He
    Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China.
  • Chen Wang
    Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Zijun Wang
    School of Chemistry and Chemical Engineering, Shihezi University Shihezi Xinjiang 832003 PR China eavanh@163.com lqridge@163.com 1175828694@qq.com 318798309@qq.com wzj_tea@shzu.edu.cn.
  • Siqi Zeng
    Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, China.
  • Qiang Huang
    Department of Orthopedics, West China Hospital, Sichuan University, Chengdu Sichuan, 610041, P.R.China.
  • Tian Guan
    Key Laboratory of Food Quality and Safety of Guangdong Province, College of Food Science, South China Agricultural University, Guangzhou, 510642, China.
  • Yonghong He
    Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China.
  • Congrong Liu
    Department of Pathology, Peking University Health Science Center, 38 College Road, Haidian, Beijing, 100191, China; Department of Pathology, School of Basic Medical Sciences, Third Hospital, Peking University Health Science Center, Beijing, 100191, China. Electronic address: congrong_liu@hsc.pku.edu.cn.

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