mlsanityData to 7 Oct 2026

MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification

arXiv 2110.14795 ↗First posted 27 Oct 2021cs.CV cs.AI cs.LGcited by 682

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Cited by 31 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce MedMNIST v2, a large-scale MNIST-like dataset collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre-processed into a small size of 28x28 (2D) or 28x28x28 (3D) with the corresponding classification labels so that no background knowledge is required for users. Covering primary data modalities in biomedical images, MedMNIST v2 is designed to perform classification on lightweight 2D and 3D images with various dataset scales (from 100 to 100,000) and diverse tasks (binary/multi-class, ordinal regression, and multi-label). The resulting dataset, consisting of 708,069 2D images and 10,214 3D images in total, could support numerous research / educational purposes in biomedical image analysis, computer vision, and machine learning. We benchmark several baseline methods on MedMNIST v2, including 2D / 3D neural networks and open-source / commercial AutoML tools. The data and code are publicly available at https://medmnist.com/.

Key facts. Cited by 682 papers in the map (data through 7 Oct 2026). Builds on 13 of its 41 references that are in the map. First posted on arXiv 27 Oct 2021.

Builds on

13 of 41 references.

References · 13

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
  4. 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 969
  5. 2019Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)cited by 657
  6. 2021The Medical Segmentation Decathloncited by 620
  7. 2017Learning multiple visual domains with residual adapterscited by 553
  8. 2019The Liver Tumor Segmentation Benchmark (LiTS)cited by 504
  9. 2019A large annotated medical image dataset for the development and evaluation of segmentation algorithmscited by 371
  10. 2020MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysiscited by 206
  11. 2018Auto-Keras: An Efficient Neural Architecture Search Systemcited by 169
  12. 2020IntrA: 3D Intracranial Aneurysm Dataset for Deep Learningcited by 41
1 more
  1. 2021A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imagingcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 37; 2023: 98; 2024: 156; 2025: 206; 2026: 184.

682 papers.

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  1. 2023PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documentscited by 212
  2. 2023BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Taskscited by 201
  3. 2024BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practicescited by 112
  4. 2022USB: A Unified Semi-supervised Learning Benchmark for Classificationcited by 95
  5. 2023MedViT: A Robust Vision Transformer for Generalized Medical Image Classificationcited by 89
  6. 2024MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicinecited by 79
  7. 2023Large AI Models in Health Informatics: Applications, Challenges, and the Futurecited by 61
  8. 2023Training on Thin Air: Improve Image Classification with Generated Datacited by 50
  9. 2022Quantum Vision Transformerscited by 48
  10. 2023Federated Conformal Predictors for Distributed Uncertainty Quantificationcited by 46
  11. 2024Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Modelscited by 43
  12. 2023Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Modelcited by 40
38 more
  1. 2022MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcarecited by 36
  2. 2023LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulationcited by 35
  3. 2025BIOMEDICA: An Open Biomedical Image-Caption Archive, Dataset, and Vision-Language Models Derived from Scientific Literaturecited by 34
  4. 2023A comprehensive survey on deep active learning in medical image analysiscited by 32
  5. 2023MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Visioncited by 31
  6. 2022GALAXY: Graph-based Active Learning at the Extremecited by 29
  7. 2024Data-Centric Foundation Models in Computational Healthcare: A Surveycited by 27
  8. 2021Personalized Federated Learning with Adaptive Batchnorm for Healthcarecited by 26
  9. 2023Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learningcited by 26
  10. 2022Making Your First Choice: To Address Cold Start Problem in Vision Active Learningcited by 25
  11. 2023Medical Vision Language Pretraining: A surveycited by 24
  12. 2024Privacy Preserving Prompt Engineering: A Surveycited by 24
  13. 2023Federated Generative Learning with Foundation Modelscited by 23
  14. 2024Residual-based Language Models are Free Boosters for Biomedical Imagingcited by 23
  15. 2025Triad: Vision Foundation Model for 3D Magnetic Resonance Imagingcited by 23
  16. 2025From Colors to Classes: Emergence of Concepts in Vision Transformerscited by 23
  17. 2023Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospectscited by 22
  18. 2023FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learningcited by 21
  19. 2024Adaptive Memory Replay for Continual Learningcited by 21
  20. 2024Distribution-Aware Data Expansion with Diffusion Modelscited by 20
  21. 2025CT-Agent: A Multimodal-LLM Agent for 3D CT Radiology Question Answeringcited by 20
  22. 2023Differentially Private Image Classification by Learning Priors from Random Processescited by 19
  23. 2023Does Continual Learning Equally Forget All Parameters?cited by 18
  24. 2024Uni-Med: A Unified Medical Generalist Foundation Model For Multi-Task Learning Via Connector-MoEcited by 18
  25. 2023Exploring the Benefits of Visual Prompting in Differential Privacycited by 17
  26. 2023Learning by Self-Explainingcited by 17
  27. 2023Limited Data, Unlimited Potential: A Study on ViTs Augmented by Masked Autoencoderscited by 17
  28. 2024SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imagingcited by 17
  29. 2024Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directionscited by 17
  30. 2022On the Importance of Hyperparameters and Data Augmentation for Self-Supervised Learningcited by 16
  31. 2023Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imagingcited by 16
  32. 2024A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Modelscited by 16
  33. 2024FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Modelscited by 16
  34. 2024Large Language Model Benchmarks in Medical Taskscited by 16
  35. 2022Quantum-classical convolutional neural networks in radiological image classificationcited by 15
  36. 2023ChAda-ViT : Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Imagescited by 15
  37. 2024Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metricscited by 15
  38. 2025SciHorizon: Benchmarking AI-for-Science Readiness from Scientific Data to Large Language Modelscited by 15

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