137 lines
8.7 KiB
Markdown
137 lines
8.7 KiB
Markdown
# Must-read papers
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**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:
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集中在超声影像和深度学习
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## [Content](#content)
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<table>
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<tr><td colspan="2"><a href="#theory">1. Theory</a></td></tr>
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<tr>
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<td> <a href="#machine-learning-theory">1.1 Machine Learning</a></td>
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<td> <a href="#deep-learning-theory">1.2 Deep Learning</a></td>
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</tr>
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<tr>
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<td> <a href="#radiomics-theory">1.3 Radiomics</a></td>
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<td></td>
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</tr>
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<tr><td colspan="2"><a href="#applications">2. Applications</a></td></tr>
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<tr>
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<td> <a href="#machine-learning">2.1 Machine Learning</a></td>
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<td> <a href="#deep-learning">2.2 Deep Learning</a></td>
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</tr>
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<tr>
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<td> <a href="#radiomics">2.3 Radiomics</a></td>
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<td> <a href="#combination">2.4 Combination</a></td>
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</tr>
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<tr><td colspan="2"><a href="servey-and-review">3. Servey And Review</a></td></tr>
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<tr><td colspan="2"><a href="related-research-platform">4. Related Research Platform</a></td></tr>
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</table>
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## [Theory](#content)
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### [Machine Learning Theory](#content)
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### [Deep Learning Theory](#content)
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### [Radiomics Theory](#content)
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## [Applications](#content)
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### [Machine Learning](#content)
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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)
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*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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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)
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*Awni Y. Hannun Pranav Rajpurkar, Masoumeh Haghpanahi, Geoffrey H. Tison, Codie Bourn, Mintu P. Turakhia and Andrew Y. Ng*
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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)
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*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*
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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)
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*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*
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### [Deep Learning](#content)
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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)
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*嵇伟伟*
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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)
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*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*
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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)
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*DAN MENG, LIBO ZHANG, GUITAO CAO, WENMING CAO, GUIXU ZHANG, AND BING HU*
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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)
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*Xiang Liu , Jia Lin Song , Shuo Hong Wang , Jing Wen Zhao and Yan Qiu Chen*
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### [Radiomics](#content)
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* #### 非超声影像
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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)
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*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*
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* #### 超声影像
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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)
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*石颉1, 2，江建伟3，常婉英3，陈曼3，张麒1, 2*
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### [Combination](#content)
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* ### Machine Learning & Radiomics
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* ### Deep Learning & Radiomics
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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)
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*Wang K, et al.*
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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)
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*赵万明*
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## [Survey And Review](#content)
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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)
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*毕 珂，王 茵*
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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)
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*赵佳琦，刁宗平，徐 琪，章建全*
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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)
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*王海星，杨志清，郭玲玲，郭燕青，张 靓，齐 昊*
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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)
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*刘盛锋，王毅，杨鑫，雷柏英，刘立，李享，倪东,汪天富*
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## [Related Research Platform](#content)
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+ [中国科学院分子影像重点实验室](http://www.radiomics.net.cn/blog/3)
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+ [Radiology](https://pubs.rsna.org/journal/radiology)
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