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Dataset Card for MedMNIST

Dataset Details

Dataset Description

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

  • 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

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

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} }

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