"Deep learning vs. conventional methods for automatic quantification of ..."

Kevin C. Ma et al. (2022)

> Home

Details and statistics

DOI: 10.1117/12.2610759

access: closed

type: Conference or Workshop Paper

metadata version: 2024-11-27

- view
  - electronic edition via DOI
  - unpaywalled version
  authority control:
- export record
  dblp key:
  - conf/mibam/MaGJSCTH22
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/conf/mibam/MaGJSCTH22
Kevin C. Ma, Michael V. Green, Elaine M. Jagoda, Jürgen Seidel, Peter L. Choyke, Baris Turkbey, Stephanie A. Harmon:
Deep learning vs. conventional methods for automatic quantification of total tumor radioactivity in positron projection images of mouse xenograft tumors. Biomedical Applications in Molecular, Structural, and Functional Imaging 2022

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.