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<td>&emsp;<a href="#radiomics-theory">1.3 Radiomics</a></td>
<td></td>
</tr>
<tr><td colspan="2"><a href="#application">2. Application</a></td></tr>
<tr><td colspan="2"><a href="#applications">2. Applications</a></td></tr>
<tr>
<td>&emsp;<a href="#machine-learning">2.1 Machine Learning</a></td>
<td>&ensp;<a href="#deep-learning">2.2 Deep Learning</a></td>
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## [Application](#content)
## [Applications](#content)
### [Machine Learning](#content)
1. **Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy** European Journal of Heart Failure (2018)
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*Awni Y. Hannun Pranav Rajpurkar, Masoumeh Haghpanahi, Geoffrey H. Tison, Codie Bourn, Mintu P. Turakhia and Andrew Y. Ng*
1. **Performance and Reading Time of Automated Breast US with or without Computer-aided** Radiology 292:540–549(2019)
*Shanling Yang, MD • Xican Gao, MD • Liwen Liu, PhD, MD • Rui Shu, MD • Jingru Yan, MD • Ge Zhang, MD • Yao Xiao, MD • Yan Ju, MS • Ni Zhao, MD • Hongping Song, PhD, MD*
### [Deep Learning](#content)
1. 基于深度学习的医学CT图像中器官的区域检测， 南京师范大学，硕士学位论文 (2018)