Unsupervised domain adaptation for vertebrae detection and identification in 3d ct volumes using a domain sanity loss

HIGHLIGHTS

• who: Pascal Sager et al. from the Centre for AI, Technikumstrasse, Zurich University of Applied Sciences, Winterthur, Switzerland have published the paper: Unsupervised Domain Adaptation for Vertebrae Detection and Identification in 3D CT Volumes Using a Domain Sanity Loss, in the Journal: (JOURNAL)
• what: The authors propose an (UDA) approach for vertebrae detection and identification based on a novel Sanity Loss (DSL) function. The authors build upon their work for the following reasons: (i) The average distance between the predicted and the actual vertebrae centroids is small and considered state-of-the-art; (ii) the . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or E-mail:

  If you don't have an account, you can create one here.