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

被引:166
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
相关论文
共 18 条
[1]   Dataset of breast ultrasound images [J].
Al-Dhabyani, Walid ;
Gomaa, Mohammed ;
Khaled, Hussien ;
Fahmy, Aly .
DATA IN BRIEF, 2020, 28
[2]  
Bilic P., 2019, The liver tumor segmentation benchmark (LiTS).
[3]  
Chollet F., 2015, KERAS 20 COMPUTER SO
[4]   Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study [J].
Faes, Livia ;
Wagner, Siegfried K. ;
Fu, Dun Jack ;
Liu, Xiaoxuan ;
Korot, Edward ;
Ledsam, Joseph R. ;
Back, Trevor ;
Chopra, Reena ;
Pontikos, Nikolas ;
Kern, Christoph ;
Moraes, Gabriella ;
Schmid, Martin K. ;
Sim, Dawn ;
Balaskas, Konstantinos ;
Bachmann, Lucas M. ;
Denniston, Alastair K. ;
Keane, Pearse A. .
LANCET DIGITAL HEALTH, 2019, 1 (05) :E232-E242
[5]  
Feurer M, 2015, ADV NEUR IN, V28
[6]  
He K, P IEEE C COMP VIS PA, P770, DOI [DOI 10.1109/CVPR.2016.90, 10.1109/CVPR.2016.90]
[7]   Auto-Keras: An Efficient Neural Architecture Search System [J].
Jin, Haifeng ;
Song, Qingquan ;
Hu, Xia .
KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2019, :1946-1956
[8]   Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study [J].
Kather, Jakob Nikolas ;
Krisam, Johannes ;
Charoentong, Pornpimol ;
Luedde, Tom ;
Herpel, Esther ;
Weis, Cleo-Aron ;
Gaiser, Timo ;
Marx, Alexander ;
Valous, Nektarios A. ;
Ferber, Dyke ;
Jansen, Lina ;
Reyes-Aldasoro, Constantino Carlos ;
Zoernig, Inka ;
Jaeger, Dirk ;
Brenner, Hermann ;
Chang-Claude, Jenny ;
Hoffmeister, Michael ;
Halama, Niels .
PLOS MEDICINE, 2019, 16 (01)
[9]  
Kermany Daniel, 2018, Mendeley Data, V3
[10]  
LeCun Y, 2010, THE MNIST DATABASE of handwritten digits, DOI DOI 10.1007/S11063-009-9095-3