MedMNIST (original) (raw)

Materials

The MedMNIST dataset consists of 12 pre-processed 2D datasets and 6 pre-processed 3D datasets from selected sources covering primary data modalities (e.g., X-Ray, OCT, Ultrasound, CT, Electron Microscope), diverse classification tasks (binary/multi-class, ordinal regression and multi-label) and data scales (from 100 to 100,000). For simplicity, we call the collection of all 2D datasets as MedMNIST2D, and that of 3D as MedMNIST3D.

We recommend our official code to download and use the MedMNIST dataset: pip install medmnist

MedMNIST2D

An Overview of MedMNIST2D in MedMNIST.Click➚ each row to view more details.

MedMNIST2D	Data Modality	Tasks (# Classes/Labels)	# Samples	# Training / Validation / Test
{{subset.dataset}}	{{subset.modality}}	{{subset.task}}	{{subset.samples}}	{{subset.splits}}

Facts of {{selected2d.dataset}}

Data Modality: {{selected2d.modality}}

Task: {{selected2d.task}}

Number of Samples: {{selected2d.samples}} ({{selected2d.splits}})

Source Data:

License: {{selected2d.license}}

MedMNIST3D

An Overview of MedMNIST3D in MedMNIST.Click➚ each row to view more details.

MedMNIST3D	Data Modality	Tasks (# Classes/Labels)	# Samples	# Training / Validation / Test
{{subset.dataset}}	{{subset.modality}}	{{subset.task}}	{{subset.samples}}	{{subset.splits}}

Facts of {{selected3d.dataset}}

Data Modality: {{selected3d.modality}}

Task: {{selected3d.task}}

Number of Samples: {{selected3d.samples}} ({{selected3d.splits}})

Source Data:

License: {{selected3d.license}}

Citation

If you find this project useful, please cite:

Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, Bingbing Ni. Yang, Jiancheng, et al. "MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification." Scientific Data, 2023.

Jiancheng Yang, Rui Shi, Bingbing Ni. "MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis". IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021.

or using bibtex:

@article{medmnistv2, 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} }

@inproceedings{medmnistv1, title={MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis}, author={Yang, Jiancheng and Shi, Rui and Ni, Bingbing}, booktitle={IEEE 18th International Symposium on Biomedical Imaging (ISBI)}, pages={191--195}, year={2021} }

Please also cite the corresponding paper(s) of source data if you use any subset of MedMNIST (check this bibtex).