A knowledge-enhanced interpretable network for early recurrence prediction of hepatocellular carcinoma via multi-phase CT imaging.

Journal: International journal of medical informatics
Published Date: Jun 1, 2024

Abstract

BACKGROUND: Predicting early recurrence (ER) of hepatocellular carcinoma (HCC) accurately can guide treatment decisions and further enhance survival. Computed tomography (CT) imaging, analyzed by deep learning (DL) models combining domain knowledge, has been employed for the prediction. However, these DL models utilized late fusion, restricting the interaction between domain knowledge and images during feature extraction, thereby limiting the prediction performance and compromising decision-making interpretability.

Authors

  • Yu Gao
    Department of Radiology Center, The First Affiliated Hospital of Xinxiang Medical University, Xin Xiang, China.
  • Xue Yang
    Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.
  • Hongjun Li
    School of Agricultural Engineering and Food Science, Shandong University of Technology, Zhangdian District, No. 12, Zhangzhou Road, Zibo, Shandong Province, China.
  • Da-Wei Ding
    School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China; Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China. Electronic address: ddaweiauto@163.com.

Keywords

External Resources

Popular Topics
Recent Journals