MedMNIST: 18x Standardized Datasets for 2D and 3D Biomedical Image Classification

Zenodo (CERN European Organization for Nuclear Research)

Abstract

Abstract We introduce MedMNIST, 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, so that no background knowledge is required for users. Covering primary data modalities in biomedical images, MedMNIST is designed to perform classification on lightweight 2D and 3D images with various data scales (from 100 to 100,000) and diverse tasks (binary/multi-class, ordinal regression and multi-label). The resulting dataset, consisting of approximately 708,000 2D images and 10,000 3D images in total, could support numerous research and educational purposes in biomedical image analysis, computer vision and machine learning. We benchmark several baseline methods on MedMNIST, including 2D / 3D neural networks and open-source / commercial AutoML tools. The data and code are publicly available at https://medmnist.com/. Note: This dataset is NOT intended for clinical use. We recommend our official code to download, parse and use the MedMNIST dataset: pip install medmnist Citation If you find this project useful, please cite both v1 and v2 paper as: 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 as per the description on the project website. License The MedMNIST dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0), except DermaMNIST under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). The code is under Apache-2.0 License. Changelog v2.2 (this repository): We have removed a small number of mistakenly included blank samples in OrganAMNIST, OrganCMNIST, OrganSMNIST, OrganMNIST3D, and VesselMNIST3D. v2.1: We have fixed the mistake in the file of NoduleMNIST3D (i.e., nodulemnist3d.npz). More details in this issue. v2.0: Initial repository of MedMNIST v2, add 6 datasets for 3D and 2 for 2D. v1.0: Initial repository of MedMNIST v1, 10 datasets for 2D.

Authors 7

1. Jiancheng Yang

   Shanghai Jiao Tong University

   Affiliation as printed

   Shanghai Jiao Tong University

2. Rui Shi

   Shanghai Jiao Tong University

   Affiliation as printed

   Shanghai Jiao Tong University

3. Donglai Wei

   Boston College · Harvard University

   Affiliation as printed

   Boston College

   Harvard University

4. Zequan Liu Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

5. Lin Fan Zhao

   Fudan University · Zhongshan Hospital

   Affiliation as printed

   Zhongshan Hospital Affiliated to Fudan University

6. Hanspeter Pfister

   Harvard University

   Affiliation as printed

   Harvard University

7. Bingbing Ni

   Shanghai Jiao Tong University

   Affiliation as printed

   Shanghai Jiao Tong University