ArticleScientific data2023
MedMNIST v2 - A large-scale lightweight benchmark for 2D and 3D biomedical image classification.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 135 papers.
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Who cites it
135 citing papers in PubMed.
- Article
- An explainable biomedical foundation model via large-scale concept-enhanced vision-language pretraining.Nature biomedical engineering · 2026Article
- Beyond Accuracy: A MultiDimensional Framework for Evaluating Medical Image Classification Through Win vs. Lose Model Comparisons.Journal of imaging informatics in medicine · 2026Article
- Review
- H-QDCT: hierarchical quantum DCT for structural-textural feature fusion in medical imaging.International journal of computer assisted radiology and surgery · 2026Article
- Beyond Size and Class Balance: Alpha as a New Dataset Quality Metric for Deep Learning.Bioengineering (Basel, Switzerland) · 2026Article
- Tina: A diffusion neural network for generating personalized AI models from text prompts.Patterns (New York, N.Y.) · 2026Article
- Embedding Anisotropy in Medical Image Foundation Models: Post Hoc Whitening Degradation and Spectral-Aware Standardization.Journal of imaging informatics in medicine · 2026Article
- Application of a deep learning method based on CBCT panoramic reconstruction images for differentiating operationally defined cystic and neoplastic jaw lesions from osteomyelitis: a single-center retrospective study.Scientific reports · 2026Article
- Weighted Sampling with Frequency-Aware Spatial Attention for Imbalanced Image Classification.Journal of imaging · 2026Article
- Efficient Uncertainty Quantification in Medical Imaging via Mamba State Space Models.Tomography (Ann Arbor, Mich.) · 2026Article
- A generalist biomedical vision-language model via multi-CLIP knowledge distillation.Nature communications · 2026Article
- Federated autoencoder-based clinical decision framework with hybrid class balancing.Scientific reports · 2026Article
- Continual Learning for Histopathology Image Classification in Class-Incremental Learning.Diagnostics (Basel, Switzerland) · 2026Article
- A lightweight deep learning model with channel attention for kidney cell classification from microscopy images.Scientific reports · 2026Article
- A ferroelectric-ionic-trapping transistor for low power and secure neuromorphic computing.Nature communications · 2026Article
- Systems-Level Support for Hybrid Quantum-Classical Learning: A Systematic Review with a Medical Imaging Translation Lens.Journal of imaging · 2026Review
- SULBA: A Task-Agnostic Data Augmentation Framework for Deep Learning in Medical Image Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Benchmarking knowledge distillation for lightweight pneumonia detection: a multi-seed calibration study on PneumoniaMNIST.BMC research notes · 2026Article
- Vision foundation model for 3D magnetic resonance imaging segmentation, classification, and registration.Medical image analysis · 2026Article
75 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
Abstract
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 28 × 28 (2D) or 28 × 28 × 28 (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 9,998 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/ .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.