  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 data in just a few lines of
code. Guided by design decisions based on gathered end-user requirements, we
implement medigan based on modular components for generative model (i)
execution, (ii) visualisation, (iii) search & ranking, and (iv) contribution.
The library's scalability and design is demonstrated by its growing number of
integrated and readily-usable pretrained generative models consisting of 21
models utilising 9 different Generative Adversarial Network architectures
trained on 11 datasets from 4 domains, namely, mammography, endoscopy, x-ray,
and MRI. Furthermore, 3 applications of medigan are analysed in this work,
which include (a) enabling community-wide sharing of restricted data, (b)
investigating generative model evaluation metrics, and (c) improving clinical
downstream tasks. In (b), extending on common medical image synthesis
assessment and reporting standards, we show Fr\'echet Inception Distance
variability based on image normalisation and radiology-specific feature
extraction.
