GaNDLF-Synth: A Framework to Democratize Generative AI for (Bio)Medical Imaging
It addresses the problem of implementing and assessing image synthesis in healthcare for researchers, though it appears incremental as a framework built on existing methods.
The paper introduces GaNDLF-Synth, a framework to democratize generative AI for biomedical imaging by providing a unified abstraction for synthesis algorithms like autoencoders, GANs, and diffusion models, aiming to lower entry barriers and enhance accessibility.
Generative Artificial Intelligence (GenAI) is a field of AI that creates new data samples from existing ones. It utilizing deep learning to overcome the scarcity and regulatory constraints of healthcare data by generating new data points that integrate seamlessly with original datasets. This paper explores the background and motivation for GenAI, and introduces the Generally Nuanced Deep Learning Framework for Synthesis (GaNDLF-Synth) to address a significant gap in the literature and move towards democratizing the implementation and assessment of image synthesis tasks in healthcare. GaNDLF-Synth describes a unified abstraction for various synthesis algorithms, including autoencoders, generative adversarial networks, and diffusion models. Leveraging the GANDLF-core framework, it supports diverse data modalities and distributed computing, ensuring scalability and reproducibility through extensive unit testing. The aim of GaNDLF-Synth is to lower the entry barrier for GenAI, and make it more accessible and extensible by the wider scientific community.