Machine Learning for Medical Imaging.
作者信息
Fu Geng-Shen, Levin-Schwartz Yuri, Lin Qiu-Hua, Zhang Da
机构信息
Amazon.com, Cambridge, USA.
Icahn School of Medicine at Mount Sinai, New York City, USA.
出版信息
J Healthc Eng. 2019 Apr 28;2019:9874591. doi: 10.1155/2019/9874591. eCollection 2019.
Abstract
摘要
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本文引用的文献
1
What can machine learning do? Workforce implications.
Science. 2017 Dec 22;358(6370):1530-1534. doi: 10.1126/science.aap8062.
2
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.
IEEE Trans Med Imaging. 2016 May;35(5):1240-1251. doi: 10.1109/TMI.2016.2538465. Epub 2016 Mar 4.
3
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning.
IEEE Trans Med Imaging. 2016 May;35(5):1285-98. doi: 10.1109/TMI.2016.2528162. Epub 2016 Feb 11.
4
Deep learning.
Nature. 2015 May 28;521(7553):436-44. doi: 10.1038/nature14539.
5
A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data.
Neuroimage. 2009 Mar;45(1 Suppl):S163-72. doi: 10.1016/j.neuroimage.2008.10.057. Epub 2008 Nov 13.
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