mlsanityData to 7 Oct 2026

MONAI: An open-source framework for deep learning in healthcare

arXiv 2211.02701 ↗First posted 4 Nov 2022cs.LG cs.AI cs.CVcited by 438

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Cited by 39 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

Key facts. Cited by 438 papers in the map (data through 7 Oct 2026). Builds on 22 of its 39 references that are in the map. First posted on arXiv 4 Nov 2022.

Builds on

22 of 39 references.

References · 22

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  4. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  5. 2016Gaussian Error Linear Units (GELUs)cited by 3,431
  6. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
  7. 2017SmoothGrad: removing noise by adding noisecited by 1,097
  8. 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 660
  9. 2021The Medical Segmentation Decathloncited by 620
  10. 2017Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentationscited by 614
  11. 2017Self-Normalizing Neural Networkscited by 591
  12. 2018Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networkscited by 278
10 more
  1. 2019Kornia: an Open Source Differentiable Computer Vision Library for PyTorchcited by 231
  2. 2015MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systemscited by 195
  3. 2020TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learningcited by 176
  4. 2020Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkitcited by 66
  5. 2021Common Limitations of Image Processing Metrics: A Picture Storycited by 58
  6. 2017NiftyNet: a deep-learning platform for medical imagingcited by 45
  7. 2022MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Imagescited by 38
  8. 2020DeepReg: a deep learning toolkit for medical image registrationcited by 16
  9. 2017DLTK: State of the Art Reference Implementations for Deep Learning on Medical Imagescited by 11
  10. 2018DeepNeuro: an open-source deep learning toolbox for neuroimagingcited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 69; 2024: 99; 2025: 145; 2026: 122.

438 papers.

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  1. 2024nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentationcited by 235
  2. 2022UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentationcited by 112
  3. 2024MAISI: Medical AI for Synthetic Imagingcited by 86
  4. 2023Generative AI for Medical Imaging: extending the MONAI Frameworkcited by 68
  5. 2024VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imagingcited by 62
  6. 2023The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiographycited by 48
  7. 2023Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Modelcited by 40
  8. 2023MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generationcited by 34
  9. 2024Large-Scale 3D Medical Image Pre-training with Geometric Context Priorscited by 34
  10. 2023Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray imagescited by 33
  11. 2023SAMM (Segment Any Medical Model): A 3D Slicer Integration to SAMcited by 31
  12. 2024Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?cited by 31
38 more
  1. 2025Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planningcited by 31
  2. 2023RadEdit: stress-testing biomedical vision models via diffusion image editingcited by 28
  3. 2024VividMed: Vision Language Model with Versatile Visual Grounding for Medicinecited by 28
  4. 2024Learning General-Purpose Biomedical Volume Representations using Randomized Synthesiscited by 27
  5. 2023DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Modelcited by 23
  6. 2025Triad: Vision Foundation Model for 3D Magnetic Resonance Imagingcited by 23
  7. 2023Unsupervised 3D out-of-distribution detection with latent diffusion modelscited by 22
  8. 2025MAISI-v2: Accelerated 3D High-Resolution Medical Image Synthesis with Rectified Flow and Region-specific Contrastive Losscited by 20
  9. 2025Better Tokens for Better 3D: Advancing Vision-Language Modeling in 3D Medical Imagingcited by 20
  10. 2023Large-scale Long-tailed Disease Diagnosis on Radiology Imagescited by 17
  11. 2025Brain Latent Progression: Individual-based Spatiotemporal Disease Progression on 3D Brain MRIs via Latent Diffusioncited by 17
  12. 2023COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-trainingcited by 16
  13. 2025PanTS: The Pancreatic Tumor Segmentation Datasetcited by 15
  14. 2023A Review of the Role of Causality in Developing Trustworthy AI Systemscited by 14
  15. 2023AnyStar: Domain randomized universal star-convex 3D instance segmentationcited by 14
  16. 2024SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRIcited by 14
  17. 2023A Generalized Surface Loss for Reducing the Hausdorff Distance in Medical Imaging Segmentationcited by 13
  18. 2024Text-Driven Tumor Synthesiscited by 13
  19. 2025Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Modelcited by 13
  20. 2025Towards Scalable Language-Image Pre-training for 3D Medical Imagingcited by 13
  21. 2024Comparative Benchmarking of Failure Detection Methods in Medical Image Segmentation: Unveiling the Role of Confidence Aggregationcited by 12
  22. 2024Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2cited by 12
  23. 2023Panoptica -- instance-wise evaluation of 3D semantic and instance segmentation mapscited by 11
  24. 2025Fit Pixels, Get Labels: Meta-learned Implicit Networks for Image Segmentationcited by 11
  25. 2023Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imagingcited by 10
  26. 2026ReCo-KD: Region- and Context-Aware Knowledge Distillation for Efficient 3D Medical Image Segmentationcited by 10
  27. 2023COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentationcited by 9
  28. 2025Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnosescited by 9
  29. 2023Domain Generalization with Adversarial Intensity Attack for Medical Image Segmentationcited by 8
  30. 2024PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRIcited by 8
  31. 2024Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT Imagingcited by 8
  32. 2024Every Component Counts: Rethinking the Measure of Success for Medical Semantic Segmentation in Multi-Instance Segmentation Taskscited by 8
  33. 2025Adapting Vision Foundation Models for Real-time Ultrasound Image Segmentationcited by 8
  34. 2025Towards Generalisable Foundation Models for Brain MRIcited by 8
  35. 2023Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualizationcited by 7
  36. 2023Comprehensive analysis of synthetic learning applied to neonatal brain MRI segmentationcited by 7
  37. 2023Fetal-BET: Brain Extraction Tool for Fetal MRIcited by 7
  38. 2025Conformal Risk Control for Semantic Uncertainty Quantification in Computed Tomographycited by 7

50 most cited of 438 All 438 (JSON)