White learning methodology: A case study of cancer-related disease factors analysis in real-time PACS environment.

Journal: Computer methods and programs in biomedicine
Published Date: Aug 26, 2020

Abstract

BACKGROUND AND OBJECTIVE: Bayesian network is a probabilistic model of which the prediction accuracy may not be one of the highest in the machine learning family. Deep learning (DL) on the other hand possess of higher predictive power than many other models. How reliable the result is, how it is deduced, how interpretable the prediction by DL mean to users, remain obscure. DL functions like a black box. As a result, many medical practitioners are reductant to use deep learning as the only tool for critical machine learning application, such as aiding tool for cancer diagnosis.

Authors

  • Tengyue Li
    Department of Computer and Information Science, University of Macau, Macau SAR. Electronic address: mb75436@um.edu.mo.
  • Simon Fong
    University of Macau, Macau.
  • Shirley W I Siu
    Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, China.
  • Xin-She Yang
    Department of Design Engineering and Mathematics, Middlesex University, London, UK. Electronic address: X.Yang@mdx.ac.uk.
  • Lian-Sheng Liu
    Department of Radiology, First Affiliated Hospital of Guangzhou University of TCM, China. Electronic address: llsjnu@sina.com.
  • Sabah Mohammed
    Department of Computer Science, Lakehead University, Thunder Bay, Canada. Electronic address: mohammed@lakeheadu.ca.

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