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---
# Dataset Card for MedMNIST
<!-- Provide a quick summary of the dataset. -->
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
MedMNIST is a large-scale MNIST-like collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre-processed into 28x28 (2D) or 28x28x28 (3D) with the corresponding classification labels.
- **License:** CC BY 4.0
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Homepage:** https://medmnist.com/
- **Paper:** Yang, J., Shi, R., Wei, D., Liu, Z., Zhao, L., Ke, B., ... & Ni, B. (2023). Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification. Scientific Data, 10(1), 41.
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
#### PathMNIST:
Total images: 107,180
Classes: 9 categories
Splits:
- **Train:** 89,996 images
- **Validation:** 10,004 images
- **Test:** 7,180 images
Image specs: 28×28 pixels
#### ChestMNIST:
Total images: 112,120
Classes: 14 categories (multi-label)
Splits:
- **Train:** 78,468 images
- **Validation:** 11,219 images
- **Test:** 22,433 images
Image specs: 28×28 pixels
#### DermaMNIST:
Total images: 10,015
Classes: 7 categories
Splits:
- **Train:** 7,007 images
- **Validation:** 1,003 images
- **Test:** 2,005 images
Image specs: 28×28 pixels
#### OCTMNIST:
Total images: 109,309
Classes: 4 categories
Splits:
- **Train:** 97,477 images
- **Validation:** 10,832 images
- **Test:** 1,000 images
Image specs: 28×28 pixels
#### PneumoniaMNIST:
Total images: 5,856
Classes: 2 categories
Splits:
- **Train:** 4,708 images
- **Validation:** 524 images
- **Test:** 624 images
Image specs: 28×28 pixels
#### RetinaMNIST:
Total images: 1,600
Classes: 5 categories (ordinal regression)
Splits:
- **Train:** 1,080 images
- **Validation:** 120 images
- **Test:** 400 images
Image specs: 28×28 pixels
#### BreastMNIST:
Total images: 780
Classes: 2 categories
Splits:
- **Train:** 546 images
- **Validation:** 78 images
- **Test:** 156 images
Image specs: 28×28 pixels
#### BloodMNIST:
Total images: 17,092
Classes: 8 categories
Splits:
- **Train:** 11,959 images
- **Validation:** 1,712 images
- **Test:** 3,421 images
Image specs: 28×28 pixels
#### TissueMNIST:
Total images: 236,386
Classes: 8 categories
Splits:
- **Train:** 165,466 images
- **Validation:** 23,640 images
- **Test:** 47,280 images
Image specs: 28×28 pixels
#### OrganAMNIST:
Total images: 58,830
Classes: 11 categories
Splits:
- **Train:** 34,561 images
- **Validation:** 6,491 images
- **Test:** 17,778 images
Image specs: 28×28 pixels
#### OrganCMNIST:
Total images: 23,583
Classes: 11 categories
Splits:
- **Train:** 12,975 images
- **Validation:** 2,392 images
- **Test:** 8,216 images
Image specs: 28×28 pixels
#### OrganSMNIST:
Total images: 25,211
Classes: 11 categories
Splits:
- **Train:** 13,932 images
- **Validation:** 2,452 images
- **Test:** 8,827 images
Image specs: 28×28 pixels
#### OrganMNIST3D:
Total images: 1,742
Classes: 11 categories
Splits:
- **Train:** 971 images
- **Validation:** 161 images
- **Test:** 610 images
Image specs: 28×28x28 pixels
#### NoduleMNIST3D:
Total images: 1,633
Classes: 2 categories
Splits:
- **Train:** 1,158 images
- **Validation:** 165 images
- **Test:** 310 images
Image specs: 28×28x28 pixels
#### AdrenalMNIST3D:
Total images: 1,584
Classes: 2 categories
Splits:
- **Train:** 1,188 images
- **Validation:** 98 images
- **Test:** 298 images
Image specs: 28×28x28 pixels
#### FractureMNIST3D:
Total images: 1,370
Classes: 3 categories
Splits:
- **Train:** 1,027 images
- **Validation:** 103 images
- **Test:** 240 images
Image specs: 28×28x28 pixels
#### VesselMNIST3D:
Total images: 1,908
Classes: 2 categories
Splits:
- **Train:** 1,335 images
- **Validation:** 191 images
- **Test:** 382 images
Image specs: 28×28x28 pixels
#### SynapseMNIST3D:
Total images: 1,759
Classes: 2 categories
Splits:
- **Train:** 1,230 images
- **Validation:** 177 images
- **Test:** 352 images
Image specs: 28×28x28 pixels
## Example Usage
Below is a quick example of how to load this dataset via the Hugging Face Datasets library.
```
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("randall-lab/medmnist", name="pathmnist", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medmnist", name="chestmnist", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medmnist", name="dermamnist", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medmnist", name="octmnist", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medmnist", name="pneumoniamnist", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medmnist", name="retinamnist", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medmnist", name="breastmnist", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medmnist", name="bloodmnist", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medmnist", name="tissuemnist", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medmnist", name="organamnist", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medmnist", name="organcmnist", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medmnist", name="organsmnist", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medmnist", name="organmnist3d", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medmnist", name="nodulemnist3d", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medmnist", name="adrenalmnist3d", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medmnist", name="fracturemnist3d", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medmnist", name="vesselmnist3d", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/medmnist", name="synapsemnist3d", split="train", trust_remote_code=True)
# Access a sample from the dataset
example = dataset[0]
image = example["image"]
label = example["label"]
image.show() # Display the image
print(f"Label: {label}")
```
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
@article{yang2023medmnist,
title={Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification},
author={Yang, Jiancheng and Shi, Rui and Wei, Donglai and Liu, Zequan and Zhao, Lin and Ke, Bilian and Pfister, Hanspeter and Ni, Bingbing},
journal={Scientific Data},
volume={10},
number={1},
pages={41},
year={2023},
publisher={Nature Publishing Group UK London}
}