HealthCare/README.md

# 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="#survey-and-review">3. Survey And Review</a></td></tr>
<tr><td colspan="2"><a href="#related-research-platform">4. Related Research Platform</a></td></tr>
 <tr><td colspan="2"><a href="#public-data">5. Public Data</a></td></tr>
</table>

## [Theory](#content)  




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




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




### [Radiomics Theory](#content)  
1. 医学成像技术，重庆大学出版社，（2005）  [*[read]*](/Theory/Radiomics/医学成像技术.pdf)

    *郭兴明*





## [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)  [*[read]*](https://github.com/vonpower/Healthcare/blob/main/Applications/Machine%20Learning/2018-Machine%20learning-based%20phenogrouping%20in%20heart%20failure%20to%20identify%20responders%20to%20cardiac%20resynchronization%20therapy.pdf)

    *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)  [*[read]*](https://github.com/vonpower/Healthcare/blob/main/Applications/Machine%20Learning/2019-Cardiologist-level%20arrhythmia%20detection%20and%20classification%20in%20ambulatory%20electrocardiograms%20using%20a%20deep%20neural%20network.pdf)

    *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)  [*[read]*](https://github.com/vonpower/Healthcare/blob/main/Applications/Machine%20Learning/2019-Performance%20and%20Reading%20Time%20of%20Automated%20Breast%20US%20with%20or%20without%20Computer-aided.pdf)

    *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)  [*[read]*](https://github.com/vonpower/Healthcare/blob/main/Applications/Machine%20Learning/Impact%20of%20Data%20Presentation%20on%20Physician%20Performance%20Utilizing%20ArtificialIntelligence-Based%20Computer-Aided%20Diagnosis%20and%20DecisionSupport%20Systems.pdf)

    *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*
    
 1. 基于支持向量机的肝脏肿瘤良、恶性识别研究，浙江大学，硕士学位论文（2012）  [*[read]*](https://github.com/vonpower/Healthcare/blob/main/Applications/Machine%20Learning/基于支持向量机的肝脏肿瘤良、恶性识别研究（2012硕士论文）.pdf)
 
     *叶萌萌*
    
### [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)  [*[read]*](https://github.com/vonpower/Healthcare/blob/main/Applications/Deep%20Learning/基于深度学习的医学CT图像中器官的区域检测_嵇伟伟.caj)

    *嵇伟伟*
    
1. **Management of Thyroid Nodules Seen on US Images:Deep Learning May Match Performance of Radiologists，**   Radiology  292:695–701(2019) [*[read]*](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*
    
1. **Liver fibrosis classification based on transfer learning and FCNet for ultrasound images，**   IEEE Access (2017) [*[read]*](/Applications/Deep%20Learning/Liver%20fibrosis%20classification%20based%20on%20transfer%20learning%20and%20FCNet%20for%20ultrasound%20images.pdf)

    *DAN MENG, LIBO ZHANG, GUITAO CAO, WENMING CAO, GUIXU ZHANG, AND BING HU*
    
1. **Learning to diagnose cirrhosis with liver capsule guided ultrasound image classification，**   Sensors (2017) [*[read]*](/Applications/Deep%20Learning/Learning%20to%20diagnose%20cirrhosis%20with%20liver%20capsule%20guided%20ultrasound%20image%20classification.pdf)

    *Xiang Liu , Jia Lin Song , Shuo Hong Wang , Jing Wen Zhao and Yan Qiu Chen*
    
1. **Breast Tumor Detection in Ultrasound Images Using Deep Learning，**   __Conference Paper__  in  Lecture Notes in Computer Science · August 2017  [*[read]*](/Applications/Deep%20Learning/Breast%20Tumor%20Detection%20in%20Ultrasound%20Images%20Using%20Deep%20Learning.pdf)

    *Zhantao Cao, Lixin Duan, Guowu Yang, Ting Yue, Qin Chen, Huazhu Fu, and Yanwu Xu* 
    
1. **Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks,**   Medical Image Analysis, 58(2019)  [*[read]*](https://github.com/vonpower/Healthcare/blob/main/Applications/Deep%20Learning/AutomatedDetectionandClassificationofThyroidNodulesinUltrasoundImagesUsingClinical-Knowledge-GuidedConvolutionalNeuralNetworks-Proof.pdf)

    *TianjiaoLiu,QianqianGuo,ChunfengLian,XuhuaRen,ShujunLiang,JingYug,LijuanNiu,WeidongSun,DinggangShen*

### [Radiomics](#content)

* #### 非超声
1. **Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer,**    Clinical Cancer Research (2017)  [*[read]*](https://github.com/vonpower/Healthcare/blob/main/Applications/Radiomics/%E9%9D%9E%E8%B6%85%E5%A3%B0%E5%BD%B1%E5%83%8F/2017-Radiomics%20Analysis%20for%20Evaluation%20of%20Pathological%20Complete%20Response%20to%20Neoadjuvant%20Chemoradiotherapy%20in%20Locally%20Advanced%20Rectal%20Cancer.pdf)

    *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
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  [*[read]*](https://github.com/vonpower/Healthcare/blob/main/Applications/Radiomics/%E8%B6%85%E5%A3%B0%E5%BD%B1%E5%83%8F/%E9%9D%A2%E5%90%91%E6%B7%8B%E5%B7%B4%E7%BB%93%E7%97%85%E5%8F%98%E5%A4%9A%E5%88%86%E7%B1%BB%E9%89%B4%E5%88%AB%E7%9A%84%E5%BC%B9%E6%80%A7%E5%92%8CB%E5%9E%8B%E5%8F%8C%E6%A8%A1%E6%80%81%E8%B6%85%E5%A3%B0%E5%BD%B1%E5%83%8F%E7%BB%84%E5%AD%A6_%E7%9F%B3%E9%A2%89.pdf)

    *石颉1, 2，江建伟3，常婉英3，陈曼3，张麒1, 2*
    
1. 实时超声造影技术诊断肾脏实性占位病变的价值，南方医科大学学报，2014  [*[read]*](https://github.com/vonpower/Healthcare/blob/main/Applications/Radiomics/_实时超声造影技术诊断肾脏实性占位病变的价值.pdf)

    *李 鑫，梁 萍，于晓玲，于 杰，程志刚，韩志宇，刘方义，穆梦娟*

### [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)  [*[read]*](https://github.com/vonpower/Healthcare/blob/main/Applications/Combination/Deep%20Learning%2BRadiomics/2018-GUT-WangKun.pdf)

    *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)  [*[read]*](https://github.com/vonpower/Healthcare/blob/main/Applications/Combination/Deep%20Learning%2BRadiomics/%E5%9F%BA%E4%BA%8E%E5%BD%B1%E5%83%8F%E7%BB%84%E5%AD%A6%E5%92%8C%E6%B7%B1%E5%BA%A6%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E7%9A%84%E8%B6%85%E5%A3%B0%E5%9B%BE%E5%83%8F%E8%82%9D%E7%BA%A4%E7%BB%B4%E5%8C%96%E8%AF%84%E4%BC%B0%E6%96%B9%E6%B3%95%E7%A0%94%E7%A9%B6_%E8%B5%B5%E4%B8%87%E6%98%8E.caj)

    *赵万明*

## [Survey And Review](#content)
1. 计算机辅助诊断技术在超声医学中的应用进展, 综述，肿瘤影像学，2019年第28卷第5期[*[read]*](/Servey%20And%20Review/计算机辅助诊断技术在超声医学中的应用进展.pdf)

    *毕　珂，王　茵*
    
1. 人工智能时代超声医学新发展，综述，第二军医大学学报，2019 年 5 月第 40 卷第 5 期[*[read]*](/Servey%20And%20Review/人工智能时代超声医学新发展.pdf)

    *赵佳琦，刁宗平，徐  琪，章建全*
    
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 [*[read]*](/Servey%20And%20Review/%E5%9F%BA%E4%BA%8E%E5%A4%A7%E6%95%B0%E6%8D%AE%E5%92%8C%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E7%9A%84%E8%B6%85%E5%A3%B0%E5%8C%BB%E5%AD%A6%E5%8F%91%E5%B1%95%E7%8E%B0%E7%8A%B6%E5%8F%8A%E9%97%AE%E9%A2%98%E7%A0%94%E7%A9%B6.pdf)

    *王海星，杨志清，郭玲玲，郭燕青，张　靓，齐　昊*
    
1. 深度学习在医学超声图像分析中的应用综述，Engineering， 5(2): 261–275 (2019) [*[read]*](http://www.engineering.org.cn/ch/10.1016/j.eng.2018.11.020)  [*[英文原文]*](https://doi.org/10.1016/j.eng.2018.11.020)

    *刘盛锋，王毅，杨鑫，雷柏英，刘立，李享，倪东,汪天富*
    
1. **A Survey on Domain Knowledge Powered Deep Learning for Medical Image Analysis**，arXiv e-prints， (2020) [*[read]*](/Servey%20And%20Review/A%20Survey%20on%20Domain%20Knowledge%20Powered%20Deep.pdf)

    *Xiaozheng Xie, Jianwei Niu, Senior Member, IEEE, Xuefeng Liu, Zhengsu Chen, and ShaojieTang, Member, IEEE*
    
## [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)
+ [混合成像系统实验室](http://www.hislab.cn/publication)
+[南佛罗里达大学工程学院计算机视觉与模式识别小组](http://www.eng.usf.edu/cvprg/)


## [Public Data](#content)
+ [ISBI（生物医学成像国际研讨会）每届数据下载地址](https://grand-challenge.org/challenges/)
+ [哈佛beamandrew机器学习和医学影像研究者贡献的数据集](https://github.com/beamandrew/medical-data)
+ [心脏病心房图像及标注数据](http://dataju.cn/Dataju/web/datasetInstanceDetail/121)
+ [癌症CT影像数据](http://dataju.cn/Dataju/web/datasetInstanceDetail/275)
+ [软组织肉瘤CT图像数据](http://dataju.cn/Dataju/web/datasetInstanceDetail/284)
+ [肺癌CT图像数据](http://dataju.cn/Dataju/web/datasetInstanceDetail/291)
+ [癌症MRI影像数据](http://dataju.cn/Dataju/web/datasetInstanceDetail/317)
+ [MICCAI胰腺分割数据集](http://medicaldecathlon.com/)
+ [The National Library of Medicine presents MedPix](https://medpix.nlm.nih.gov/home)
+ [结肠癌CT数据](https://wiki.cancerimagingarchive.net/display/Public/CT+COLONOGRAPHY#dc149b9170f54aa29e88f1119e25ba3e)
+ [AMRG Cardiac Atlas（心脏MRI图像）](http://www.cardiacatlas.org/studies/amrg-cardiac-atlas/)
+ [大脑MRI数据集](http://www.oasis-brains.org/)
+ [肺部图像数据库联盟](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI)
+ [INbreast：数字化乳腺摄影数据库](http://medicalresearch.inescporto.pt/breastresearch/index.php/Get_INbreast_Database)
+ [前列腺癌数据集](http://www.ehealthlab.cs.ucy.ac.cy/index.php/facilities/32-software/218-datasets)
+ [DeepLesion:多类别、病灶级别标注临床医疗CT图像开放数据集（230G）](https://academictorrents.com/details/de50f4d4aa3d028944647a56199c07f5fa6030ff)
+ [MURA:基于深度学习检测骨骼疾病(吴恩达团队公布)](https://www.groundai.com/project/mura-dataset-towards-radiologist-level-abnormality-detection-in-musculoskeletal-radiographs/3)