mlsanityData to 14 Oct 2026

Generative AI for Medical Imaging: extending the MONAI Framework

arXiv 2307.15208 ↗First posted 27 Jul 2023eess.IV cs.CVcited by 68

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Cited by 29 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to help safely share medical data via synthetic datasets but also to perform an array of diverse applications, such as anomaly detection, image-to-image translation, denoising, and MRI reconstruction. However, due to the complexity of these models, their implementation and reproducibility can be difficult. This complexity can hinder progress, act as a use barrier, and dissuade the comparison of new methods with existing works. In this study, we present MONAI Generative Models, a freely available open-source platform that allows researchers and developers to easily train, evaluate, and deploy generative models and related applications. Our platform reproduces state-of-art studies in a standardised way involving different architectures (such as diffusion models, autoregressive transformers, and GANs), and provides pre-trained models for the community. We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas. Finally, we adopt a modular and extensible approach, ensuring long-term maintainability and the extension of current applications for future features.

Key facts. Cited by 68 papers in the map (data through 14 Oct 2026). Builds on 43 of its 59 references that are in the map. First posted on arXiv 27 Jul 2023.

Builds on

43 of 59 references.

References · 43

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
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31 more
  1. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  2. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,253
  3. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,694
  4. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,299
  5. 2017High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANscited by 1,179
  6. 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,163
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  9. 2021The Medical Segmentation Decathloncited by 628
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  11. 2023BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairscited by 520
  12. 2022MONAI: An open-source framework for deep learning in healthcarecited by 448
  13. 2023Synthetic Data from Diffusion Models Improves ImageNet Classificationcited by 327
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  16. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 273
  17. 2022Brain Imaging Generation with Latent Diffusion Modelscited by 268
  18. 2022Diffusion Models for Medical Anomaly Detectioncited by 232
  19. 2021Diffusion Models for Implicit Image Segmentation Ensemblescited by 198
  20. 2023StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learnerscited by 177
  21. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstructioncited by 118
  22. 2022Denoising diffusion models for out-of-distribution detectioncited by 104
  23. 2023Generative models improve fairness of medical classifiers under distribution shiftscited by 90
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  25. 2022What is Healthy? Generative Counterfactual Diffusion for Lesion Localizationcited by 52
  26. 2023DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Modelscited by 51
  27. 2022Can segmentation models be trained with fully synthetically generated data?cited by 37
  28. 2022Transformer-based out-of-distribution detection for clinically safe segmentationcited by 16
  29. 2022Morphology-preserving Autoregressive 3D Generative Modelling of the Braincited by 15
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 31; 2025: 23; 2026: 9.

68 papers.

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  1. 2023MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Imagescited by 41
  2. 2024A Survey on Data Synthesis and Augmentation for Large Language Modelscited by 41
  3. 2025A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontierscited by 37
  4. 2024I2I-Mamba: Multi-modal medical image synthesis via selective state space modelingcited by 26
  5. 2025Brain Latent Progression: Individual-based Spatiotemporal Disease Progression on 3D Brain MRIs via Latent Diffusioncited by 17
  6. 2023MAM-E: Mammographic synthetic image generation with diffusion modelscited by 11
  7. 2024Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Modelscited by 11
  8. 2024Unconditional Latent Diffusion Models Memorize Patient Imaging Data: Implications for Openly Sharing Synthetic Datacited by 9
  9. 2024Diff-Def: Diffusion-Generated Deformation Fields for Conditional Atlasescited by 8
  10. 2025Regression is all you need for medical image translationcited by 8
  11. 2024Zero-Shot Medical Phrase Grounding with Off-the-shelf Diffusion Modelscited by 7
  12. 2024Synthetic Data in Radiological Imaging: Current State and Future Outlookcited by 7
38 more
  1. 2024Latent Causal Modeling for 3D Brain MRI Counterfactualscited by 7
  2. 2023Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networkscited by 6
  3. 2024When Diffusion MRI Meets Diffusion Model: A Novel Deep Generative Model for Diffusion MRI Generationcited by 6
  4. 2024Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networkscited by 6
  5. 2024CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentationcited by 5
  6. 2025Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstructioncited by 5
  7. 2025CoCoLIT: ControlNet-Conditioned Latent Image Translation for MRI to Amyloid PET Synthesiscited by 5
  8. 2025Generative AI in Depth: A Survey of Recent Advances, Model Variants, and Real-World Applicationscited by 5
  9. 2026BrainWorld: A Structural-Prior-Conditioned Generative Model for Whole-Brain 4D fMRI Dynamicscited by 5
  10. 2024SynCellFactory: Generative Data Augmentation for Cell Trackingcited by 4
  11. 2024RadGazeGen: Radiomics and Gaze-guided Medical Image Generation using Diffusion Modelscited by 4
  12. 2024Anatomical feature-prioritized loss for enhanced MR to CT translationcited by 4
  13. 2024Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric Ensemblescited by 4
  14. 2025BrainMRDiff: A Diffusion Model for Anatomically Consistent Brain MRI Synthesiscited by 4
  15. 2025Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical Imagingcited by 4
  16. 2024qMRI Diffuser: Quantitative T1 Mapping of the Brain using a Denoising Diffusion Probabilistic Modelcited by 3
  17. 2024Volumetric Conditioning Module to Control Pretrained Diffusion Models for 3D Medical Imagescited by 3
  18. 2024Denoising Diffusion Models for Anomaly Localization in Medical Imagescited by 3
  19. 2025Radiology Report Conditional 3D CT Generation with Multi Encoder Latent diffusion Modelcited by 3
  20. 2023Histogram- and Diffusion-Based Medical Out-of-Distribution Detectioncited by 2
  21. 2024Rapid Review of Generative AI in Smart Medical Applicationscited by 2
  22. 2024X-ray2CTPA: Leveraging Diffusion Models to Enhance Pulmonary Embolism Classificationcited by 2
  23. 2024Knowledge-driven AI-generated data for accurate and interpretable breast ultrasound diagnosescited by 2
  24. 2024VQalAttent: a Transparent Speech Generation Pipeline based on Transformer-learned VQ-VAE Latent Spacecited by 2
  25. 2025Towards Better Cephalometric Landmark Detection with Diffusion Data Generationcited by 2
  26. 2025Neural Autoregressive Modeling of Brain Agingcited by 2
  27. 2025MRpro: open framework for model-based, learned, and quantitative MR imagingcited by 2
  28. 2025Scalable Diffusion Transformer for Conditional 4D fMRI Synthesiscited by 2
  29. 2023Computational limits to the legibility of the imaged human braincited by 1
  30. 2024Towards the Discovery of Down Syndrome Brain Biomarkers Using Generative Modelscited by 1
  31. 2024Conditioning 3D Diffusion Models with 2D Images: Towards Standardized OCT Volumes through En Face-Informed Super-Resolutioncited by 1
  32. 2024SynStitch: a Self-Supervised Learning Network for Ultrasound Image Stitching Using Synthetic Training Pairs and Indirect Supervisioncited by 1
  33. 2025Cascaded 3D Diffusion Models for Whole-body 3D 18-F FDG PET/CT synthesis from Demographicscited by 1
  34. 2026Tokenizer-Generator Coupling in Medical Image Generationcited by 1
  35. 2026Beyond Representation Learning: A Systematic Study of Joint-Embedding Predictive Generation for 3D Brain MRIcited by 1
  36. 2026Generating Chest X-Ray Counterfactuals by Specialising Foundation Image Modelscited by 1
  37. 2024IgCONDA-PET: Weakly-Supervised PET Anomaly Detection using Implicitly-Guided Attention-Conditional Counterfactual Diffusion Modeling -- a Multi-Center, Multi-Cancer, and Multi-Tracer Studycited by 0
  38. 2024Generative artificial intelligence in ophthalmology: multimodal retinal images for the diagnosis of Alzheimer's disease with convolutional neural networkscited by 0

50 most cited of 68 All 68 (JSON)

Similar, not cited

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  1. 2025Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation
  2. 2024Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges
  3. 2023Recent Advances in Generative AI for Healthcare Applications
  4. 2023Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review
  5. 2024A Generalist Model for Diverse Text-Guided Medical Image Synthesis
  6. 2024Deep Generative Models for 3D Medical Image Synthesis
  7. 2024Multi-Conditioned Denoising Diffusion Probabilistic Model (mDDPM) for Medical Image Synthesis
  8. 2022Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation