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

Sarthak Pati, Szymon Mazurek, Spyridon Bakas

TL;DR

GaNDLF-Synth describes a unified abstraction for various synthesis algorithms, including autoencoders, generative adversarial networks, and diffusion models, and supports diverse data modalities and distributed computing, ensuring scalability and reproducibility through extensive unit testing.

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.

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

TL;DR

GaNDLF-Synth describes a unified abstraction for various synthesis algorithms, including autoencoders, generative adversarial networks, and diffusion models, and supports diverse data modalities and distributed computing, ensuring scalability and reproducibility through extensive unit testing.

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.
Paper Structure (14 sections, 2 figures, 1 table)

This paper contains 14 sections, 2 figures, 1 table.

Table of Contents

  1. Background
  2. Summary
  3. Discussion
  4. Resource Availability
  5. Summary Statement
  6. Code Location
  7. Potential Use Cases
  8. Licensing
  9. Ethical Considerations
  10. Methods
  11. Software Stack and Flowchart
  12. Functionalities
  13. Validation
  14. Code Reproducibility

Figures (2)

  • Figure 1: High-level illustrations for (a) Autoencoders (AE), (b) Generative Adversarial networks (GAN), and (c) Diffusion models.
  • Figure 2: Illustration showcasing the internal components of GaNDLF-Syth. (a) is showcasing the software stack, with special focus on the usage of existing functionalities from PyTorch and GaNDLF for compute functionality and user experience, and leveraging a customizable interface to allow easy algorithmic extensions. (b) is showcasing the flowchart depicting the overall training and inference procedure offered in GaNDLF-Synth.