Light&fast generative adversarial network for high-fidelity CT image synthesis of liver tumor.

Journal: Computer methods and programs in biomedicine
Published Date: May 28, 2024

Abstract

BACKGROUND AND OBJECTIVE: Hepatocellular carcinoma is a common disease with high mortality. Through deep learning methods to analyze HCC CT, the screening classification and prognosis model of HCC can be established, which further promotes the development of computer-aided diagnosis and treatment in the treatment of HCC. However, there are significant challenges in the actual establishment of HCC auxiliary diagnosis model due to data imbalance, especially for rare subtypes of HCC and underrepresented demographic groups. This study proposes a GAN model aimed at overcoming these obstacles and improving the accuracy of HCC auxiliary diagnosis.

Authors

  • Zechen Zheng
    Department of Interventional Therapy, Guangdong Provincial Hospital of Chinese, Medicine and Guangdong Provincial Academy of Chinese Medical Sciences, No. 111 Dade Road, Guangzhou, 510080, Guangdong, People's Republic of China.
  • Miao Wang
    Public Affairs Department, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Chao Fan
    College of Management Science, Chengdu University of Technology, Chengdu, China.
  • Congqian Wang
    The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Northwest University, Xi'an, 710127, China; School of Network and Data Center, Northwest University, Xi'an, 710127, China.
  • Xuelei He
  • Xiaowei He

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