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 708K 2D images and 10K 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.
Update: We are thrilled to release MedMNIST+ with larger sizes: 64x64, 128x128, and 224x224 for 2D, and 64x64x64 for 3D. As a complement to the previous 28-size MedMNIST, the large-size version could serve as a standardized benchmark for medical foundation models.
Please note that this dataset is NOT intended for clinical use.
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 | Data Modality | Tasks (# Classes/Labels) | # Samples | # Training / Validation / Test |
---|---|---|---|---|
PathMNIST | Colon Pathology | Multi-Class (9) | 107,180 | 89,996 / 10,004 / 7,180 |
ChestMNIST | Chest X-Ray | Multi-Label (14) Binary-Class (2) | 112,120 | 78,468 / 11,219 / 22,433 |
DermaMNIST | Dermatoscope | Multi-Class (7) | 10,015 | 7,007 / 1,003 / 2,005 |
OCTMNIST | Retinal OCT | Multi-Class (4) | 109,309 | 97,477 / 10,832 / 1,000 |
PneumoniaMNIST | Chest X-Ray | Binary-Class (2) | 5,856 | 4,708 / 524 / 624 |
RetinaMNIST | Fundus Camera | Ordinal Regression (5) | 1,600 | 1,080 / 120 / 400 |
BreastMNIST | Breast Ultrasound | Binary-Class (2) | 780 | 546 / 78 / 156 |
BloodMNIST | Blood Cell Microscope | Multi-Class (8) | 17,092 | 11,959 / 1,712 / 3,421 |
TissueMNIST | Kidney Cortex Microscope | Multi-Class (8) | 236,386 | 165,466 / 23,640 / 47,280 |
OrganAMNIST | Abdominal CT | Multi-Class (11) | 58,830 | 34,561 / 6,491 / 17,778 |
OrganCMNIST | Abdominal CT | Multi-Class (11) | 23,583 | 12,975 / 2,392 / 8,216 |
OrganSMNIST | Abdominal CT | Multi-Class (11) | 25,211 | 13,932 / 2,452 / 8,827 |
Jakob Nikolas Kather, Johannes Krisam, et al., "Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study," PLOS Medicine, vol. 16, no. 1, pp. 1–22, 01 2019.
License: CC BY 4.0MedMNIST3D | Data Modality | Tasks (# Classes/Labels) | # Samples | # Training / Validation / Test |
---|---|---|---|---|
OrganMNIST3D | Abdominal CT | Multi-Class (11) | 1,742 | 971 / 161 / 610 |
NoduleMNIST3D | Chest CT | Binary-Class (2) | 1,633 | 1,158 / 165 / 310 |
AdrenalMNIST3D | Shape from Abdominal CT | Binary-Class (2) | 1,584 | 1,188 / 98 / 298 |
FractureMNIST3D | Chest CT | Multi-Class (3) | 1,370 | 1,027 / 103 / 240 |
VesselMNIST3D | Shape from Brain MRA | Binary-Class (2) | 1,908 | 1,335 / 191 / 382 |
SynapseMNIST3D | Electron Microscope | Binary-Class (2) | 1,759 | 1,230 / 177 / 352 |
Patrick Bilic, Patrick Ferdinand Christ, et al., "The liver tumor segmentation benchmark (lits)," arXiv preprint arXiv:1901.04056, 2019.
Xuanang Xu, Fugen Zhou, et al., "Efficient multiple organ localization in ct image using 3d region proposal network," IEEE Transactions on Medical Imaging, vol. 38, no. 8, pp. 1885–1898, 2019.
License: CC BY 4.0Methods | 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 | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
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 | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
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. 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).
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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.
Copyright © 2020-2024 MedMNIST Team
This website created by Jiancheng Yang is hosted on GitHub. Updated on Jan, 2024.