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# Must-read papers
**本仓库主要分享在医疗影像（CT/核磁/超声）处理领域值得一读的文章**:blush:
**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)
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## [Application related](#content)
### [Machine Learning](#content)
#### 有监督学习
* #### 有监督学习
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*
#### 无监督学习
* #### 无监督学习
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*
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### [Radiomics](#content)
#### 非超声影像
* #### 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)
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
* #### 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
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)
#### 硕士学位论文
* #### 硕士学位论文
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 (2018)
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 (2018)
*嵇伟伟*
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 (2019)
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 (2019)
*赵万明*