Improving image labelling quality

被引：1
|
作者：
Day, Thomas G. [1 ,2 ]
Simpson, John M. [1 ,2 ]
Razavi, Reza [1 ,2 ]
Kainz, Bernhard [3 ,4 ]
机构：
[1] Guys & St Thomas NHS Fdn Trust, Dept Congenital Cardiol, Evelina London Childrens Healthcare, London, England
[2] Kings Coll London, Fac Life Sci & Med, Sch Biomed Engn & Imaging Sci, London, England
[3] Friedrich Alexander Univ Erlangen Nurnberg, Dept Artificial Intelligence Biomed Engn, Erlangen, Germany
[4] Imperial Coll London, Fac Engn, Dept Comp, London, England
来源：
NATURE MACHINE INTELLIGENCE | 2023年 / 5卷 / 04期
关键词：
D O I：
10.1038/s42256-023-00645-1
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
There is a continuing demand for high-quality, large-scale annotated datasets in medical imaging supported by machine learning. A new study investigates the importance of what type of instructions crowdsourced annotators receive.
引用
收藏
页码：335 / 336
页数：2
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