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.

翻译：近年来，生成式AI的进步在多个领域带来了革命性突破，其中医学影像尤为突出。这类生成模型不仅能够通过合成数据集安全共享医疗数据，还可在异常检测、图像到图像翻译、去噪以及MRI重建等多种应用中发挥巨大潜力。然而，由于模型的高度复杂性，其实现与可复现性存在较大困难，这可能阻碍研究进展、形成应用壁垒，并妨碍新方法与现有工作的比较。本研究提出MONAI生成模型——一个免费开源平台，使研究人员和开发者能够轻松训练、评估和部署生成模型及相关应用。该平台以标准化方式复现了涉及不同架构（如扩散模型、自回归Transformer和生成对抗网络）的最新研究，并向社区提供预训练模型。我们以泛化方式实现这些模型，证明其结果可扩展至2D或3D场景，涵盖不同模态（如CT、MRI和X射线数据）及不同解剖区域的医学影像。最后，我们采用模块化且可扩展的设计方法，确保长期可维护性并支持未来功能的扩展。