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

• URL: http://arxiv.org/abs/2010.14925v4
• Date: Thu, 20 May 2021 22:47:30 GMT
• Title: MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis
• Authors: Jiancheng Yang, Rui Shi, Bingbing Ni
• Abstract summary: We present MedMNIST, a collection of 10 pre-processed medical open datasets. MedMNIST is standardized to perform classification tasks on lightweight 28x28 images. MedMNIST could be used for educational purpose, rapid prototyping, multi-modal machine learning or AutoML in medical image analysis.
• Score: 46.02653153307692
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present MedMNIST, a collection of 10 pre-processed medical open datasets. MedMNIST is standardized to perform classification tasks on lightweight 28x28 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. The datasets, evaluation code and baseline methods for MedMNIST are publicly available at https://medmnist.github.io/.

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