medigan: A Python Library of Pretrained Generative Models for Enriched Data Access in Medical Imaging. (arXiv:2209.14472v1 [eess.IV])

Synthetic data generated by generative models can enhance the performance and
capabilities of data-hungry deep learning models in medical imaging. However,
there is (1) limited availability of (synthetic) datasets and (2) generative
models are complex to train, which hinders their adoption in research and
clinical applications. To reduce this entry barrier, we propose medigan, a
one-stop shop for pretrained generative models implemented as an open-source
framework-agnostic Python library. medigan allows researchers and developers to
create, increase, and domain-adapt their training …

arxiv data data access generative models imaging library medical medical imaging python