MEDMNIST CLASSIFICATION DECATHLON: A LIGHTWEIGHT AUTOML BENCHMARK FOR MEDICAL IMAGE ANALYSIS

被引:198
作者
Yang, Jiancheng [1 ,2 ]
Shi, Rui [1 ]
Ni, Bingbing [1 ,2 ]
机构
[1] Shanghai Jiao Tong Univ, Shanghai 200240, Peoples R China
[2] Shanghai Jiao Tong Univ, AI Inst, MoE Key Lab Artificial Intelligence, Shanghai, Peoples R China
来源
2021 IEEE 18TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI) | 2021年
基金
美国国家科学基金会;
关键词
MedMNIST; AutoML; Classification; Multi-Modal; Benchmark; Decathlon;
D O I
10.1109/ISBI48211.2021.9434062
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
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/.(1)
引用
收藏
页码:191 / 195
页数:5
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