Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging

Daniel Wolf, Tristan Payer, Catharina Silvia Lisson, Christoph Gerhard Lisson, Meinrad Beer, Michael Götz, Timo Ropinski

Open source

DOI
10.1038/s41598-023-46433-0
Published
2023-08-12T11:31:01Z
Container
Not recorded
Publisher
arXiv
Open access
yes

Credibility signals

uncertain Score 53/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

Cite this work

BibTeX

@article{allodium:10.1038/s41598-023-46433-0,
  title = {Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging},
  author = {Daniel Wolf and Tristan Payer and Catharina Silvia Lisson and Christoph Gerhard Lisson and Meinrad Beer and Michael Götz and Timo Ropinski},
  year = {2023},
  doi = {10.1038/s41598-023-46433-0},
  url = {https://doi.org/10.1038/s41598-023-46433-0}
}

RIS

TY  - JOUR
TI  - Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging
AU  - Daniel Wolf
AU  - Tristan Payer
AU  - Catharina Silvia Lisson
AU  - Christoph Gerhard Lisson
AU  - Meinrad Beer
AU  - Michael Götz
AU  - Timo Ropinski
PY  - 2023
DO  - 10.1038/s41598-023-46433-0
UR  - https://doi.org/10.1038/s41598-023-46433-0
ER  -

APA

Wolf, D., Payer, T., Lisson, C. S., Lisson, C. G., Beer, M., Götz, M., & Ropinski, T. (2023). Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging. https://doi.org/10.1038/s41598-023-46433-0

Source records