# Must-read papers
**本仓库主要分享AI结合医疗影像(CT/核磁/超声)领域值得一读的文章和资源**:blush:
集中在超声影像和深度学习
## [Content](#content)
## [Theory](#content)
### [Machine Learning Theory](#content)
### [Deep Learning Theory](#content)
### [Radiomics Theory](#content)
## [Application](#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)
*Maja Cikes1, Sergio Sanchez-Martinez, Brian Claggett, Nicolas Duchateau, Gemma Piella, Constantine Butakoff, Anne Catherine Pouleur, Dorit Knappe, Tor Biering-Sørensen, Valentina Kutyifa, Arthur Moss, Kenneth Stein, Scott D. Solomon, and Bart Bijnens*
1. **Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network** Nature Medicine 25, 65–69(2019)
*Awni Y. Hannun Pranav Rajpurkar, Masoumeh Haghpanahi, Geoffrey H. Tison, Codie Bourn, Mintu P. Turakhia and Andrew Y. Ng*
### [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)
*嵇伟伟*
### [Radiomics](#content)
* #### 非超声影像
1. **Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer,** Clinical Cancer Research (2017)
*Zhenyu Liu, Xiao-Yan Zhang,Yan-Jie Shi, Lin Wang, Hai-Tao Zhu, Zhenchao Tang, Shuo Wang, Xiao-Ting Li, Jie Tian, and Ying-Shi Sun*
* #### 超声影像
1. 面向淋巴结病变多分类鉴别的弹性和 B 型 双模态超声影像组学, 生物医学工程学杂志,2019年12月第36卷第6期
*石颉1, 2,江建伟3,常婉英3,陈曼3,张麒1, 2*
### [Combination](#content)
* ### Machine Learning & Radiomics
* ### Deep Learning & Radiomics
1. **Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study,** GUT (2018)
*Wang K, et al.*
1. 基于影像组学和深度迁移学习的超声图像肝纤维化评估方法研究, 深圳大学,硕士学位论文 (2019)
*赵万明*
## [Related Research Platform](#content)
+ [中国科学院分子影像重点实验室](http://www.radiomics.net.cn/blog/3)