"Effect of observer variability and training cases on U-Net segmentation ..."

Jordan D. Fuhrman et al. (2020)

> Home

Details and statistics

DOI: 10.1117/12.2549118

access: closed

type: Conference or Workshop Paper

metadata version: 2021-04-09

- view
  - electronic edition via DOI
  - unpaywalled version
  authority control:
- export record
  dblp key:
  - conf/miipop/FuhrmanHYJHYG20
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/conf/miipop/FuhrmanHYJHYG20
Jordan D. Fuhrman, Peter Halloran, Rowena Yip, Artit C. Jirapatnakul, Claudia I. Henschke, David F. Yankelevitz, Maryellen L. Giger:
Effect of observer variability and training cases on U-Net segmentation performance. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2020: 113160T

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.