"Federating Unlabeled Samples: A Semi-supervised Collaborative Framework ..."

Laëtitia Launet et al. (2022)

> Home

Details and statistics

DOI: 10.1007/978-3-031-21753-1_7

access: closed

type: Conference or Workshop Paper

metadata version: 2022-11-22

- view
  - electronic edition via DOI
  - unpaywalled version
  authority control:
- export record
  dblp key:
  - conf/ideal/LaunetACMMMZN22
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/conf/ideal/LaunetACMMMZN22
Laëtitia Launet, Rocío del Amor, Adrián Colomer, Andrés Mosquera-Zamudio, Anaïs Moscardó, Carlos Monteagudo, Zhiming Zhao, Valery Naranjo:
Federating Unlabeled Samples: A Semi-supervised Collaborative Framework for Whole Slide Image Analysis. IDEAL 2022: 64-72

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.