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

• URL: http://arxiv.org/abs/2410.00173v1
• Date: Mon, 30 Sep 2024 19:25:01 GMT
• Title: GaNDLF-Synth: A Framework to Democratize Generative AI for (Bio)Medical Imaging
• Authors: Sarthak Pati, Szymon Mazurek, Spyridon Bakas,
• Abstract summary: Generative Artificial Intelligence (GenAI) is a field of AI that creates new data samples from existing ones. This paper explores the background and motivation for GenAI, and introduces the Generally Nuanced Deep Learning Framework for Synthesis (GaNDLF- Synth) GaNDLF- Synth describes a unified abstraction for various algorithms, including autoencoders, generative adversarial networks, and synthesis models.
• Score: 0.36638033546156024
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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