# 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: ## [Content](#content)
1. Tool related
1.1 Machine Learning 1.2 Deep Learning
1.3 Radiomics
2. Application related
2.1 Machine Learning 2.2 Deep Learning
2.3 Radiomics 2.4 Combination
3. Related Research Platform
## [Tool related](#content) ### [Machine Learning Theory](#content) ### [Deep Learning Theory](#content) ### [Radiomics Theory](#content) ## [Application related](#content) ### [Machine Learning](#content) #### 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 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* ### [Deep Learning](#content) ### [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) #### 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) *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) *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)