An accurate and trustworthy deep learning approach for bladder tumor segmentation with uncertainty estimation.

Journal: Computer methods and programs in biomedicine
PMID: 39954510

Abstract

BACKGROUND AND OBJECTIVE: Although deep learning-based intelligent diagnosis of bladder cancer has achieved excellent performance, the reliability of neural network predicted results may not be evaluated. This study aims to explore a trustworthy AI-based tumor segmentation model, which not only outputs predicted results but also provides confidence information about the predictions.

Authors

  • Jie Xu
    Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310000, China.
  • Haixin Wang
    Sichuan Provincial Key Laboratory for Human Disease Gene Study, Center for Medical Genetics, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
  • Min Lu
    Hangzhou Science and Technology Information Institute, Hangzhou, Zhejiang, China.
  • Hai Bi
    Department of Urology, Peking University Third Hospital, Beijing 100191, China. Electronic address: bihai@bjmu.edu.cn.
  • Deng Li
    Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201620, China.
  • Zixuan Xue
    Department of Clinical Laboratory, Key Laboratory of Clinical Laboratory Diagnosis and Translational Research of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
  • Qi Zhang
    Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China.

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