mlsanityData to 14 Oct 2026

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

arXiv 2410.00173 ↗First posted 30 Sep 2024cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 59 references that are in the map. First posted on arXiv 30 Sep 2024.

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References · 26

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,299
  4. 2022MONAI: An open-source framework for deep learning in healthcarecited by 448
  5. 2016Learning from Simulated and Unsupervised Images through Adversarial Trainingcited by 288
  6. 2023A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPTcited by 224
  7. 2022Metrics reloaded: Recommendations for image analysis validationcited by 195
  8. 2018Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networkscited by 147
  9. 2022Machine Learning Operations (MLOps): Overview, Definition, and Architecturecited by 128
  10. 2016Medical Image Synthesis with Context-Aware Generative Adversarial Networkscited by 91
  11. 2021MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluationcited by 88
  12. 2022Federated Learning Enables Big Data for Rare Cancer Boundary Detectioncited by 84
14 more
  1. 2018GANs for Medical Image Analysiscited by 81
  2. 2023Generative AI for Medical Imaging: extending the MONAI Frameworkcited by 68
  3. 2021OpenFL: An open-source framework for Federated Learningcited by 58
  4. 2022Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generationcited by 54
  5. 2021The Federated Tumor Segmentation (FeTS) Challengecited by 50
  6. 2024Generative Artificial Intelligence: A Systematic Review and Applicationscited by 37
  7. 2021GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imagingcited by 25
  8. 2022On Optimizing the Communication of Model Parallelismcited by 19
  9. 2016MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classificationcited by 16
  10. 2023Recent Advances in Generative AI for Healthcare Applicationscited by 7
  11. 2021How Do Software Developers Use GitHub Actions to Automate Their Workflows?cited by 6
  12. 2023From paintbrush to pixel: A review of deep neural networks in AI-generated artcited by 5
  13. 2022Federated Learning for the Classification of Tumor Infiltrating Lymphocytescited by 4
  14. 2023Developers' Perception of GitHub Actions: A Survey Analysiscited by 4

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  6. 2023Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review
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