Recent advances in generative AI have brought incredible breakthroughs in
several areas, including medical imaging. These generative models have
tremendous potential not only to help safely share medical data via synthetic
datasets but also to perform an array of diverse applications, such as anomaly
detection, image-to-image translation, denoising, and MRI reconstruction.
However, due to the complexity of these models, their implementation and
reproducibility can be difficult. This complexity can hinder progress, act as a
use barrier, and dissuade the comparison of new methods with existing works. In
this study, we present MONAI Generative Models, a freely available open-source
platform that allows researchers and developers to easily train, evaluate, and
deploy generative models and related applications. Our platform reproduces
state-of-art studies in a standardised way involving different architectures
(such as diffusion models, autoregressive transformers, and GANs), and provides
pre-trained models for the community. We have implemented these models in a
generalisable fashion, illustrating that their results can be extended to 2D or
3D scenarios, including medical images with different modalities (like CT, MRI,
and X-Ray data) and from different anatomical areas. Finally, we adopt a
modular and extensible approach, ensuring long-term maintainability and the
extension of current applications for future features.

最近在生成 AI 方面取得的突破已经在多个领域，包括医学成像方面，取得了令人难以置信的进展。我们提出了 MONAI 生成模型，这是一个开放源代码的平台，可以帮助研究人员和开发人员轻松训练、评估和部署生成模型及相关应用。