Summary of Gandlf-synth: a Framework to Democratize Generative Ai For (bio)medical Imaging, by Sarthak Pati et al.
GaNDLF-Synth: A Framework to Democratize Generative AI for (Bio)Medical Imaging
by Sarthak Pati, Szymon Mazurek, Spyridon Bakas
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a new framework called Generally Nuanced Deep Learning Framework for Synthesis (GaNDLF-Synth) that aims to democratize image synthesis tasks in healthcare. The framework is designed to overcome the scarcity and regulatory constraints of healthcare data by generating new data points that integrate seamlessly with original datasets. GaNDLF-Synth uses deep learning to create a unified abstraction for various synthesis algorithms, including autoencoders, generative adversarial networks, and diffusion models. This enables scalability, reproducibility, and extensibility through extensive unit testing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores the background and motivation for Generative Artificial Intelligence (GenAI) in healthcare, which creates new data samples from existing ones to overcome data scarcity and regulatory constraints. The GaNDLF-Synth framework is designed to make GenAI more accessible and extensible by the wider scientific community. It describes a unified abstraction for various synthesis algorithms and supports diverse data modalities and distributed computing. |
Keywords
» Artificial intelligence » Deep learning » Image synthesis
