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| # Dataset Card for MedMNIST |
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| <!-- Provide a quick summary of the dataset. --> |
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| ## Dataset Details |
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| ### Dataset Description |
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| <!-- Provide a longer summary of what this dataset is. --> |
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| 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. |
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| - **License:** CC BY 4.0 |
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| ### Dataset Sources |
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| <!-- Provide the basic links for the dataset. --> |
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| - **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. |
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| ## Dataset Structure |
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| <!-- 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. --> |
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| #### PathMNIST: |
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| Total images: 107,180 |
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| Classes: 9 categories |
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| Splits: |
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| - **Train:** 89,996 images |
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| - **Validation:** 10,004 images |
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| - **Test:** 7,180 images |
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| Image specs: 28×28 pixels |
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| #### ChestMNIST: |
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| Total images: 112,120 |
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| Classes: 14 categories (multi-label) |
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| Splits: |
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| - **Train:** 78,468 images |
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| - **Validation:** 11,219 images |
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| - **Test:** 22,433 images |
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| Image specs: 28×28 pixels |
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| #### DermaMNIST: |
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| Total images: 10,015 |
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| Classes: 7 categories |
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| Splits: |
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| - **Train:** 7,007 images |
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| - **Validation:** 1,003 images |
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| - **Test:** 2,005 images |
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| Image specs: 28×28 pixels |
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| #### OCTMNIST: |
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| Total images: 109,309 |
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| Classes: 4 categories |
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| Splits: |
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| - **Train:** 97,477 images |
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| - **Validation:** 10,832 images |
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| - **Test:** 1,000 images |
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| Image specs: 28×28 pixels |
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| #### PneumoniaMNIST: |
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| Total images: 5,856 |
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| Classes: 2 categories |
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| Splits: |
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| - **Train:** 4,708 images |
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| - **Validation:** 524 images |
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| - **Test:** 624 images |
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| Image specs: 28×28 pixels |
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| #### RetinaMNIST: |
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| Total images: 1,600 |
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| Classes: 5 categories (ordinal regression) |
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| Splits: |
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| - **Train:** 1,080 images |
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| - **Validation:** 120 images |
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| - **Test:** 400 images |
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| Image specs: 28×28 pixels |
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| #### BreastMNIST: |
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| Total images: 780 |
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| Classes: 2 categories |
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| Splits: |
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| - **Train:** 546 images |
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| - **Validation:** 78 images |
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| - **Test:** 156 images |
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| Image specs: 28×28 pixels |
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| #### BloodMNIST: |
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| Total images: 17,092 |
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| Classes: 8 categories |
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| Splits: |
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| - **Train:** 11,959 images |
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| - **Validation:** 1,712 images |
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| - **Test:** 3,421 images |
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| Image specs: 28×28 pixels |
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| #### TissueMNIST: |
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| Total images: 236,386 |
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| Classes: 8 categories |
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| Splits: |
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| - **Train:** 165,466 images |
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| - **Validation:** 23,640 images |
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| - **Test:** 47,280 images |
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| Image specs: 28×28 pixels |
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| #### OrganAMNIST: |
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| Total images: 58,830 |
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| Classes: 11 categories |
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| Splits: |
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| - **Train:** 34,561 images |
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| - **Validation:** 6,491 images |
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| - **Test:** 17,778 images |
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| Image specs: 28×28 pixels |
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| #### OrganCMNIST: |
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| Total images: 23,583 |
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| Classes: 11 categories |
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| Splits: |
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| - **Train:** 12,975 images |
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| - **Validation:** 2,392 images |
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| - **Test:** 8,216 images |
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| Image specs: 28×28 pixels |
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| #### OrganSMNIST: |
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| Total images: 25,211 |
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| Classes: 11 categories |
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| Splits: |
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| - **Train:** 13,932 images |
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| - **Validation:** 2,452 images |
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| - **Test:** 8,827 images |
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| Image specs: 28×28 pixels |
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| #### OrganMNIST3D: |
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| Total images: 1,742 |
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| Classes: 11 categories |
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| Splits: |
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| - **Train:** 971 images |
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| - **Validation:** 161 images |
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| - **Test:** 610 images |
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| Image specs: 28×28x28 pixels |
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| #### NoduleMNIST3D: |
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| Total images: 1,633 |
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| Classes: 2 categories |
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| Splits: |
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| - **Train:** 1,158 images |
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| - **Validation:** 165 images |
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| - **Test:** 310 images |
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| Image specs: 28×28x28 pixels |
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| #### AdrenalMNIST3D: |
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| Total images: 1,584 |
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| Classes: 2 categories |
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| Splits: |
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| - **Train:** 1,188 images |
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| - **Validation:** 98 images |
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| - **Test:** 298 images |
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| Image specs: 28×28x28 pixels |
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| #### FractureMNIST3D: |
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| Total images: 1,370 |
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| Classes: 3 categories |
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| Splits: |
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| - **Train:** 1,027 images |
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| - **Validation:** 103 images |
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| - **Test:** 240 images |
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| Image specs: 28×28x28 pixels |
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| #### VesselMNIST3D: |
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| Total images: 1,908 |
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| Classes: 2 categories |
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| Splits: |
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| - **Train:** 1,335 images |
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| - **Validation:** 191 images |
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| - **Test:** 382 images |
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| Image specs: 28×28x28 pixels |
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| #### SynapseMNIST3D: |
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| Total images: 1,759 |
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| Classes: 2 categories |
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| Splits: |
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| - **Train:** 1,230 images |
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| - **Validation:** 177 images |
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| - **Test:** 352 images |
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| Image specs: 28×28x28 pixels |
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| ## Example Usage |
| Below is a quick example of how to load this dataset via the Hugging Face Datasets library. |
| ``` |
| from datasets import load_dataset |
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| # 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"] |
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| image.show() # Display the image |
| print(f"Label: {label}") |
| ``` |
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| ## Citation |
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| <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
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| **BibTeX:** |
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| @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} |
| } |
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