A multi-constraint representation learning model for identification of ovarian cancer with missing laboratory indicators.

Journal: Nan fang yi ke da xue xue bao = Journal of Southern Medical University
PMID: 39819725

Abstract

OBJECTIVES: To evaluate the performance of a multi-constraint representation learning classification model for identifying ovarian cancer with missing laboratory indicators.

Authors

  • Zihan Lu
    School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
  • Fangjun Huang
    School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
  • Guangyao Cai
    National Clinical Research Center for Obstetrics and Gynaecology, Cancer Biology Research Centre (Key Laboratory of the Ministry of Education) and Department of Gynaecology and Obstetrics, Tongji Hospital, Wuhan, China.
  • Jihong Liu
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Xin Zhen
    Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, United States of America.

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