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MedMNIST

Lightweight Benchmark for Medical Image Analysis

MedMNIST

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.

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Shanghai Jiao Tong University, Shanghai, China
View author website
Task
Image Classification
Annotation Types
Classification Tags
720000
Items
18
Classes
720000
Labels
Models using this dataset
Last updated on 
January 20, 2022
Licensed under 
MIT
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