We introduce MedMNIST v2, 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 28 x 28 (2D) or 28 x 28 x 28 (3D) with the corresponding classification labels, so that no background knowledge is required for users. Covering primary data modalities in biomedical images, MedMNIST v2 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 708,069 2D images and 9,998 3D images in total, could support numerous research / educational purposes in biomedical image analysis, computer vision and machine learning. We benchmark several baseline methods on MedMNIST v2, including 2D / 3D neural networks and open-source / commercial AutoML tools.
Please note that this dataset is NOT intended for clinical use.
The MedMNIST v2 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 | Data Modality | Tasks (# Classes/Labels) | # Samples | # Training / Validation / Test |
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License: {{selected2d.license}}MedMNIST3D | Data Modality | Tasks (# Classes/Labels) | # Samples | # Training / Validation / Test |
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License: {{selected3d.license}}Methods | PathMNIST | ChestMNIST | DermaMNIST | OCTMNIST | PneumoniaMNIST | RetinaMNIST | ||||||
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AUC | ACC | AUC | ACC | AUC | ACC | AUC | ACC | AUC | ACC | AUC | ACC | |
ResNet-18 (28) | 0.983 | 0.907 | 0.768 | 0.947 | 0.917 | 0.735 | 0.943 | 0.743 | 0.944 | 0.854 | 0.717 | 0.524 |
ResNet-18 (224) | 0.989 | 0.909 | 0.773 | 0.947 | 0.920 | 0.754 | 0.958 | 0.763 | 0.956 | 0.864 | 0.710 | 0.493 |
ResNet-50 (28) | 0.990 | 0.911 | 0.769 | 0.947 | 0.913 | 0.735 | 0.952 | 0.762 | 0.948 | 0.854 | 0.726 | 0.528 |
ResNet-50 (224) | 0.989 | 0.892 | 0.773 | 0.948 | 0.912 | 0.731 | 0.958 | 0.776 | 0.962 | 0.884 | 0.716 | 0.511 |
auto-sklearn | 0.934 | 0.716 | 0.649 | 0.779 | 0.902 | 0.719 | 0.887 | 0.601 | 0.942 | 0.855 | 0.690 | 0.515 |
AutoKeras | 0.959 | 0.834 | 0.742 | 0.937 | 0.915 | 0.749 | 0.955 | 0.763 | 0.947 | 0.878 | 0.719 | 0.503 |
Google AutoML Vision | 0.944 | 0.728 | 0.778 | 0.948 | 0.914 | 0.768 | 0.963 | 0.771 | 0.991 | 0.946 | 0.750 | 0.531 |
Methods | BreastMNIST | BloodMNIST | TissueMNIST | OrganAMNIST | OrganCMNIST | OrganSMNIST | ||||||
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AUC | ACC | AUC | ACC | AUC | ACC | AUC | ACC | AUC | ACC | AUC | ACC | |
ResNet-18 (28) | 0.901 | 0.863 | 0.998 | 0.958 | 0.930 | 0.676 | 0.997 | 0.935 | 0.992 | 0.900 | 0.972 | 0.782 |
ResNet-18 (224) | 0.891 | 0.833 | 0.998 | 0.963 | 0.933 | 0.681 | 0.998 | 0.951 | 0.994 | 0.920 | 0.974 | 0.778 |
ResNet-50 (28) | 0.857 | 0.812 | 0.997 | 0.956 | 0.931 | 0.680 | 0.997 | 0.935 | 0.992 | 0.905 | 0.972 | 0.770 |
ResNet-50 (224) | 0.866 | 0.842 | 0.997 | 0.950 | 0.932 | 0.680 | 0.998 | 0.947 | 0.993 | 0.911 | 0.975 | 0.785 |
auto-sklearn | 0.836 | 0.803 | 0.984 | 0.878 | 0.828 | 0.532 | 0.963 | 0.762 | 0.976 | 0.829 | 0.945 | 0.672 |
AutoKeras | 0.871 | 0.831 | 0.998 | 0.961 | 0.941 | 0.703 | 0.994 | 0.905 | 0.990 | 0.879 | 0.974 | 0.813 |
Google AutoML Vision | 0.919 | 0.861 | 0.998 | 0.966 | 0.924 | 0.673 | 0.990 | 0.886 | 0.988 | 0.877 | 0.964 | 0.749 |
Methods | OrganMNIST3D | NoduleMNIST3D | FractureMNIST3D | AdrenalMNIST3D | VesselMNIST3D | SynapseMNIST3D | ||||||
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AUC | ACC | AUC | ACC | AUC | ACC | AUC | ACC | AUC | ACC | AUC | ACC | |
ResNet-18 + 2.5D | 0.977 | 0.788 | 0.838 | 0.835 | 0.587 | 0.451 | 0.718 | 0.772 | 0.748 | 0.846 | 0.634 | 0.696 |
ResNet-18 + 3D | 0.996 | 0.907 | 0.863 | 0.844 | 0.712 | 0.508 | 0.827 | 0.721 | 0.874 | 0.877 | 0.820 | 0.745 |
ResNet-18 + ACS | 0.994 | 0.900 | 0.873 | 0.847 | 0.714 | 0.497 | 0.839 | 0.754 | 0.930 | 0.928 | 0.705 | 0.722 |
ResNet-50 + 2.5D | 0.974 | 0.769 | 0.835 | 0.848 | 0.552 | 0.397 | 0.732 | 0.763 | 0.751 | 0.877 | 0.669 | 0.735 |
ResNet-50 + 3D | 0.994 | 0.883 | 0.875 | 0.847 | 0.725 | 0.494 | 0.828 | 0.745 | 0.907 | 0.918 | 0.851 | 0.795 |
ResNet-50 + ACS | 0.994 | 0.889 | 0.886 | 0.841 | 0.750 | 0.517 | 0.828 | 0.758 | 0.912 | 0.858 | 0.719 | 0.709 |
auto-sklearn | 0.977 | 0.814 | 0.914 | 0.874 | 0.628 | 0.453 | 0.828 | 0.802 | 0.910 | 0.915 | 0.631 | 0.730 |
AutoKeras | 0.979 | 0.804 | 0.844 | 0.834 | 0.642 | 0.458 | 0.804 | 0.705 | 0.773 | 0.894 | 0.538 | 0.724 |
If you find this project useful, please cite:
Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, Bingbing Ni. "MedMNIST v2: A Large-Scale Lightweight Benchmark for 2D and 3D Biomedical Image Classification". arXiv preprint arXiv:2110.14795, 2021. 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={arXiv preprint arXiv:2110.14795}, year={2021} } @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 (bibtex).
The dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).
The code is under Apache-2.0 License.
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