Bone metastasis scintigram generation using generative adversarial learning with multi-receptive field learning and two-stage training.

Journal: Medical physics
Published Date: Sep 3, 2024

Abstract

BACKGROUND: Deep learning is the primary method for conducting automated analysis of SPECT bone scintigrams. The lack of available large-scale data significantly hinders the development of well-performing deep learning models, as the performance of a deep learning model is positively correlated with the size of the dataset used. Therefore, there is an urgent demand for an automated data generation method to enlarge the dataset of SPECT bone scintigrams.

Authors

  • Qiang Lin
    College of Sciences, Zhejiang University of Technology, China.
  • An Xie
    Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, China.
  • Xianwu Zeng
    Department of Nuclear Medicine, Gansu Provincial Cancer Hospital, Lanzhou, China.
  • Yongchun Cao
    School of Mathematics and Computer Science, Northwest Minzu University, No. 1, Xibei Xincun Rd., Lanzhou, 730030, Gansu, China.
  • Zhengxing Man
    School of Mathematics and Computer Science, Northwest Minzu University, No. 1, Xibei Xincun Rd., Lanzhou, 730030, Gansu, China.
  • Yusheng Hao
    School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, China.
  • Caihong Liu
    Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, China.
  • Xiaodi Huang
    School of Computing and Mathematics, Charles Sturt University, Albury, NSW 2640, Australia.

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