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

作者：Jiancheng Yang, Rui Shi, Bingbing Ni · 年份：2021 · DOI：10.1109/isbi48211.2021.9434062 · 被引用次数：358 · 研究领域：AI in cancer detection、Radiomics and Machine Learning in Medical Imaging、COVID-19 diagnosis using AI

We present MedMNIST, a collection of 10 pre-processed medical open datasets. MedMNIST is standardized to perform classification tasks on lightweight 28 x 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. The datasets, evaluation code and baseline methods for MedMNIST are publicly available at https://medmnist.github.io/.