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😊
集中在超声影像和深度学习

Content

1. Theory
1.1 Machine Learning	1.2 Deep Learning
1.3 Radiomics
2. Applications
2.1 Machine Learning	2.2 Deep Learning
2.3 Radiomics	2.4 Combination
3. Literature review
4. Related Research Platform

Theory

Machine Learning Theory

Deep Learning Theory

Radiomics Theory

Applications

Machine Learning

1. Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy, European Journal of Heart Failure (2018) *[read]*

   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

2. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network, Nature Medicine 25, 65–69(2019) *[read]*

   Awni Y. Hannun Pranav Rajpurkar, Masoumeh Haghpanahi, Geoffrey H. Tison, Codie Bourn, Mintu P. Turakhia and Andrew Y. Ng

3. Performance and Reading Time of Automated Breast US with or without Computer-aided, Radiology 292:540–549(2019) *[read]*

   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

4. 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]*

   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

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]*

   嵇伟伟

2. Management of Thyroid Nodules Seen on US Images:Deep Learning May Match Performance of Radiologists， Radiology 292:695–701(2019) *[read]*

   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

3. Liver fibrosis classification based on transfer learning and FCNet for ultrasound images， IEEE (2017) [[read]](/Applications/Deep%20Learning/Liver fibrosis classification based on transfer learning and FCNet for ultrasound images.pdf)

   DAN MENG, LIBO ZHANG, GUITAO CAO, WENMING CAO, GUIXU ZHANG, AND BING HU

4. Learning to diagnose cirrhosis with liver capsule guided ultrasound image classification， Sensors (2017) [[read]](/Applications/Deep%20Learning/Learning to diagnose cirrhosis with liver capsule guided ultrasound image classification.pdf)

   *Xiang Liu , Jia Lin Song , Shuo Hong Wang , Jing Wen Zhao and Yan Qiu Chen *

Radiomics

• 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) *[read]*

   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 *[read]*

   石颉1, 2，江建伟3，常婉英3，陈曼3，张麒1, 2

Combination

• 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]*

   Wang K, et al.

2. 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]*

   赵万明

Literature review

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]*

   毕　珂，王　茵

2. 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]*

   赵佳琦，刁宗平，徐  琪，章建全

3. 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]*

   王海星，杨志清，郭玲玲，郭燕青，张　靓，齐　昊

Related Research Platform

• 中国科学院分子影像重点实验室
• Radiology