GaNDLF-Synth: A Framework to Democratize Generative AI for (Bio)Medical Imaging

TL;DR

GaNDLF-Synth is a unified framework designed to democratize and facilitate the development, assessment, and application of generative AI models in healthcare imaging, promoting accessibility and reproducibility.

Contribution

It introduces a comprehensive, scalable, and extensible framework that unifies various generative algorithms for medical imaging, supporting diverse data types and distributed computing.

Findings

Supports multiple synthesis algorithms including autoencoders, GANs, and diffusion models

Ensures scalability and reproducibility through extensive testing

Facilitates wider adoption of GenAI in healthcare research

Abstract

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.