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Abstract

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 10,214 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.


Key Features

  • Diverse: It covers diverse data modalities, dataset scales (from 100 to 100,000), and tasks (binary/multi-class, multi-label, and ordinal regression). It is as diverse as the VDD and MSD to fairly evaluate the generalizable performance of machine learning algorithms in different settings, but both 2D and 3D biomedical images are provided.
  • Standardized: Each sub-dataset is pre-processed into the same format, which requires no background knowledge for users. As an MNIST-like dataset collection to perform classification tasks on small images, it primarily focuses on the machine learning part rather than the end-to-end system. Furthermore, we provide standard train-validation-test splits for all datasets in MedMNIST v2, therefore algorithms could be easily compared.
  • Lightweight: The small size of 28×28 (2D) or 28×28×28 (3D) is friendly to evaluate machine learning algorithms.
  • Educational: As an interdisciplinary research area, biomedical image analysis is difficult to hand on for researchers from other communities, as it requires background knowledge from computer vision, machine learning, biomedical imaging, and clinical science. Our data with the Creative Commons (CC) License is easy to use for educational purposes.

Please note that this dataset is NOT intended for clinical use.


Materials

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

An Overview of MedMNIST2D in MedMNIST v2. Click➚ each row to view more details.
MedMNIST2D Data Modality Tasks (# Classes/Labels) # Samples # Training / Validation / Test
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MedMNIST3D

An Overview of MedMNIST3D in MedMNIST v2. Click➚ each row to view more details.
MedMNIST3D Data Modality Tasks (# Classes/Labels) # Samples # Training / Validation / Test
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Benchmarking

Methods PathMNIST ChestMNIST DermaMNIST OCTMNIST PneumoniaMNIST RetinaMNIST
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
Benchmarking Performance on MedMNIST2D.
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
Benchmarking Performance on MedMNIST3D.
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 0.885 0.903 0.587 0.451 0.718 0.772 0.748 0.846 0.634 0.696
ResNet-18 + 3D 0.996 0.907 0.915 0.908 0.712 0.508 0.827 0.721 0.874 0.877 0.820 0.745
ResNet-18 + ACS 0.994 0.900 0.888 0.910 0.714 0.497 0.839 0.754 0.930 0.928 0.705 0.722
ResNet-50 + 2.5D 0.974 0.769 0.861 0.911 0.552 0.397 0.732 0.763 0.751 0.877 0.669 0.735
ResNet-50 + 3D 0.994 0.883 0.902 0.910 0.725 0.494 0.828 0.745 0.907 0.918 0.851 0.795
ResNet-50 + ACS 0.994 0.889 0.924 0.906 0.750 0.517 0.828 0.758 0.912 0.858 0.719 0.709
auto-sklearn 0.977 0.814 0.872 0.926 0.628 0.453 0.828 0.802 0.910 0.915 0.631 0.730
AutoKeras 0.979 0.804 0.847 0.902 0.642 0.458 0.804 0.705 0.773 0.894 0.538 0.724

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. "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).


License

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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