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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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