MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis

Jiancheng Yang      Rui Shi      Bingbing Ni      Bilian Ke     
Shanghai Jiao Tong University, Shanghai, China     
Paper [ISBI'21]       Code [Github]       Dataset [Zenodo]


We present MedMNIST, a collection of 10 pre-processed medical open datasets. MedMNIST is standardized to perform classification tasks on lightweight 28 * 28 images, which requires no background knowledge. Covering the primary data modalities in medical image analysis, it is diverse on data scale (from 100 to 100,000) and tasks (binary/multi-class, ordinal regression and multi-label). MedMNIST could be used for educational purpose, rapid prototyping, multi-modal machine learning or AutoML in medical image analysis. Moreover, MedMNIST Classification Decathlon is designed to benchmark AutoML algorithms on all 10 datasets; We have compared several baseline methods, including open-source or commercial AutoML tools.

Key Features

  • Educational: Our multi-modal data, from multiple open medical image datasets with Creative Commons (CC) Licenses, is easy to use for educational purpose.
  • Standardized: Data is pre-processed into same format, which requires no background knowledge for users.
  • Diverse: The multi-modal datasets covers diverse data scales (from 100 to 100,000) and tasks (binary/multiclass, ordinal regression and multi-label).
  • Lightweight: The small size of 28 × 28 is friendly for rapid prototyping and experimenting multi-modal machine learning and AutoML algorithms.
  • Please note that this dataset is NOT intended for clinical use.


Please download the dataset(s) via Zenodo. You could also use our code to download automatically.


An Overview of MedMNIST Dataset
Name Data Modality Tasks (# Classes/Labels) # Training # Validation # Test
PathMNIST Pathology Multi-Class (9) 89,996 10,004 7,180
ChestMNIST Chest X-ray Multi-Label (14) Binary-Class (2) 78,468 11,219 22,433
DermaMNIST Dermatoscope Multi-Class (7) 7,007 1,003 2,005
OCTMNIST OCT Multi-Class (4) 97,477 10,832 1,000
PneumoniaMNIST Chest X-ray Binary-Class (2) 4,708 524 624
RetinaMNIST Fundus Camera Ordinal Regression (5) 1,080 120 400
BreastMNIST Breast Ultrasound Binary-Class (2) 546 78 156
OrganMNIST_Axial Abdominal CT Multi-Class (11) 34,581 6,491 17,778
OragnMNIST_Coronal Abdominal CT Multi-Class (11) 13,000 2,392 8,268
OrganMNIST_Sagittal Abdominal CT Multi-Class (11) 13,940 2,452 8,829

Performance Analysis

Citation and Licenses

If you find this project useful, please cite our ISBI'21 paper as:
     Jiancheng Yang, Rui Shi, Bingbing Ni. "MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis," arXiv preprint arXiv:2010.14925, 2020.

or using bibtex:
         title={MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis},
         author={Yang, Jiancheng and Shi, Rui and Ni, Bingbing},
         journal={arXiv preprint arXiv:2010.14925},

Besides, please cite the corresponding paper if you use any subset of MedMNIST. Each subset uses the same license as that of the source dataset.


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 4.0


Xiaosong Wang, Yifan Peng, et al., "Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases," in CVPR, 2017, pp. 3462–3471.

License: CC0 1.0


Philipp Tschandl, Cliff Rosendahl, and Harald Kittler, "The ham10000 dataset, a large collection of multisource dermatoscopic images of common pigmented skin lesions," Scientific data, vol. 5, pp. 180161, 2018.

License: CC BY-NC 4.0


Daniel S. Kermany, Michael Goldbaum, et al., "Identifying medical diagnoses and treatable diseases by image-based deep learning," Cell, vol. 172, no. 5, pp. 1122 – 1131.e9, 2018.

License: CC BY 4.0


DeepDR Diabetic Retinopathy Image Dataset (DeepDRiD), "The 2nd diabetic retinopathy – grading and image quality estimation challenge,", 2020.

License: CC BY 4.0


Walid Al-Dhabyani, Mohammed Gomaa, Hussien Khaled, and Aly Fahmy, "Dataset of breast ultrasound images," Data in Brief, vol. 28, pp. 104863, 2020.

License: CC BY 4.0


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 4.0

Copyright © 2020- MedMNIST Team

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