A quantitative analysis of the improvement provided by comprehensive annotation on CT lesion detection using deep learning.

Journal: Journal of applied clinical medical physics
Published Date: Jul 30, 2024

Abstract

BACKGROUND: Data collected from hospitals are usually partially annotated by radiologists due to time constraints. Developing and evaluating deep learning models on these data may result in over or under estimation PURPOSE: We aimed to quantitatively investigate how the percentage of annotated lesions in CT images will influence the performance of universal lesion detection (ULD) algorithms.

Authors

  • Jingchen Ma
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China; Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032.
  • Jin H Yoon
    Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA New York Presbyterian Hospital, New York, NY 10032, USA.
  • Lin Lu
    School of Economics and Management, Guangxi Normal University, Guilin, China.
  • Hao Yang
    College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China.
  • Pingzhen Guo
    Department of Radiology, Columbia University Medical Center/New York Presbyterian Hospital, New York, New York.
  • Dawei Yang
    Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital Fudan University, Shanghai, China.
  • Jing Li
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Jingxian Shen
    Department of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China.
  • Lawrence H Schwartz
    Department of Radiology, Columbia University College of Physicians and Surgeons, New York, NY, USA.
  • Binsheng Zhao
    Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032.

Keywords

External Resources

Popular Topics
Recent Journals