MedMNIST/MedMNIST resource

Health v2 · maintenance only

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1398 stars · 213 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

MedMNIST is a collection of 18 standardized biomedical image classification datasets (12 2D and 6 3D), available in multiple sizes from MNIST-like 28px up to 224px, with a pip-installable Python API. It serves as a lightweight benchmark for medical image analysis, machine learning research, and education.

Use cases

• benchmark medical image classification models
• find a lightweight MNIST-like dataset for biomedical deep learning
• get standardized 2D and 3D medical images for teaching machine learning
• evaluate AutoML tools on medical imaging tasks
• train models on multi-label and ordinal regression medical image tasks
• benchmark medical foundation models on larger 64/128/224 images

When to choose

• you need standardized, ready-to-use biomedical image datasets without domain preprocessing
• you want a lightweight benchmark covering diverse modalities, scales, and task types
• you are doing education, prototyping, or fair model comparison in medical imaging

When to avoid

• you need full-resolution clinical images or raw DICOM data
• you need pixel-level segmentation or detection annotations rather than classification labels
• your production system requires real-world clinical validation data

Facets

dataset · maturity stable

machine-learning deep-learning image-processing benchmarking data-science machine-learning computer-vision healthcare bioinformatics artificial-intelligence python cross-platform medical-imaging mnist-like image-classification biomedical pytorch automl-benchmark 2d-3d-images few-shot-learning

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