Generative AI for Medical Imaging: extending the MONAI Framework
Walter H. L. Pinaya Mark S. Graham Eric Kerfoot Petru-Daniel Tudosiu Jessica Dafflon Virginia Fernandez Pedro Sanchez Julia Wolleb Pedro F. da Costa Ashay Patel Hyungjin Chung Can Zhao Wei Peng Zelong Liu Xueyan Mei Oeslle Lucena Jong Chul Ye Sotirios A. Tsaftaris Prerna Dogra Andrew Feng Marc Modat Parashkev Nachev Sebastien Ourselin M. Jorge Cardoso
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.