# 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)

<table>
<tr><td colspan="2"><a href="#theory">1. Theory</a></td></tr> 
<tr>
    <td>&emsp;<a href="#machine-learning-theory">1.1 Machine Learning</a></td>
    <td>&ensp;<a href="#deep-learning-theory">1.2 Deep Learning</a></td>
</tr>
<tr>
    <td>&emsp;<a href="#radiomics-theory">1.3 Radiomics</a></td>
    <td></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>
</tr>
<tr>
    <td>&emsp;<a href="#radiomics">2.3 Radiomics</a></td>
    <td>&ensp;<a href="#combination">2.4 Combination</a></td>
</tr>
<tr><td colspan="2"><a href="related-research-platform">3. Related Research Platform</a></td></tr>
</table>

## [Theory](#content)  




### [Machine Learning Theory](#content)  




### [Deep Learning Theory](#content)  




### [Radiomics Theory](#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)

    *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*
    
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*
    
1. **Impact of Data Presentation on Physician Performance Utilizing ArtificialIntelligence-Based Computer-Aided Diagnosis and DecisionSupport Systems,**   Journal of Digital Imaging 32:408–416 (2019)

    *L. Barinov1,2,3 A. Jairaj1 M. Becker3,4 SSeymour1 E. Lee3,4 A. Schram3,4&E. Lane4&A. Goldszal3,4 D. Quigley4 L. Paster3,4*
    
### [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*
    
1. 基于大数据和人工智能的超声医学发展现状及问题研究， 综述，肿瘤影像学，2020年第29卷第4期  
[[paper]](Healthcare/Applications/Deep Learning/基于大数据和人工智能的超声医学发展现状及问题研究.pdf)

    *王海星1，杨志清1，郭玲玲1，郭燕青1，张　靓1，齐　昊1，2*
    
1. **Management of Thyroid Nodules Seen on US Images:Deep Learning May Match Performance of Radiologists，**   Radiology  292:695–701(2019)   
[[paper]](https://doi.org/10.1148/radiol.2019181343)

    *Mateusz Buda, MSc • Benjamin Wildman-Tobriner, MD • Jenny K. Hoang, MBBS, MHS • David Thayer, PhD, MD •Franklin N. Tessler, MD • William D. Middleton, MD • Maciej A. Mazurowski, PhD*

### [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)
+ [Radiology](https://pubs.rsna.org/journal/radiology)