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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. Survey And Review
4. Related Research Platform
5. Public Data

Theory

Machine Learning Theory

Deep Learning Theory

Radiomics Theory

  1. 医学成像技术，重庆大学出版社，（2005） *[read]*

    郭兴明

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

  5. 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]*

    叶萌萌

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 Access (2017) *[read]*

    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]*

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

  5. Breast Tumor Detection in Ultrasound Images Using Deep Learning， Conference Paper in Lecture Notes in Computer Science · August 2017 *[read]*

    Zhantao Cao, Lixin Duan, Guowu Yang, Ting Yue, Qin Chen, Huazhu Fu, and Yanwu Xu

  6. Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks, Medical Image Analysis, 58(2019) *[read]*

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

  7. Ensembled deep learning model outperforms human experts in diagnosing biliary atresia from sonographic gallbladder images, nature communications, 2021 *[read]*

    Wenying Zhou, Yang Yang, Cheng Yu, Juxian Liu and Xingxing Duan et al.

  8. Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks, Medical Image Analysis, 2019, *[read]*

    Tianjiao Liu, Qianqian Guo, Chunfeng Lian et al.

Radiomics

  • 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]*

    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]*

    石颉1, 2，江建伟3，常婉英3，陈曼3，张麒1, 2

  2. 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]*

    李 鑫，梁 萍，于晓玲，于 杰，程志刚，韩志宇，刘方义，穆梦娟

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

  3. Multiple-correlation similarity for block-matching based fast CT to ultrasound registration in liver interventions，Medical Image Analysis， (2019) *[read]*

    Jyotirmoy Banerjee, Yuanyuan Sun, Camiel Klink, Theo van Walsum, et al.

  4. PAIP 2019: Liver cancer segmentation challenge，Medical Image Analysis， (2021) *[read]*

    Yoo Jung Kim, Hyungjoon Jang, Kyoungbun Lee, Jinwook Choi, et al.

Survey And 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

  4. 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]* [英文原文]*

    刘盛锋，王毅，杨鑫，雷柏英，刘立，李享，倪东,汪天富

  5. A Survey on Domain Knowledge Powered Deep Learning for Medical Image Analysis，arXiv e-prints， (2020) *[read]*

    Xiaozheng Xie, Jianwei Niu, Senior Member, IEEE, Xuefeng Liu, Zhengsu Chen, and ShaojieTang, Member, IEEE

Related Research Platform

Public Data