Deep learning for cardiovascular medicine: a practical primer.
暂无分享,去创建一个
Kipp W. Johnson | J. Halperin | S. Narayan | U. Baber | R. Rosenson | C. Krittanawong | Zhen Wang | W. W. Tang | Mehmet Aydar | J. Min
[1] Jane E. Marshall,et al. Artificial Intelligence and Echocardiography: A Primer for Cardiac Sonographers , 2020, Journal of the American Society of Echocardiography.
[2] G. Lip,et al. Role for machine learning in sex-specific prediction of successful electrical cardioversion in atrial fibrillation? , 2020, Open Heart.
[3] Giuseppe Jurman,et al. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone , 2020, BMC Medical Informatics and Decision Making.
[4] Chang-ming Huang,et al. Application of preoperative artificial neural network based on blood biomarkers and clinicopathological parameters for predicting long-term survival of patients with gastric cancer , 2019, World journal of gastroenterology.
[5] Claudio Napoli,et al. Network Medicine: A Clinical Approach for Precision Medicine and Personalized Therapy in Coronary Heart Disease , 2019, Journal of atherosclerosis and thrombosis.
[6] Andy Kitchen,et al. Chest Radiographs in Congestive Heart Failure: Visualizing Neural Network Learning. , 2019, Radiology.
[7] Masoumeh Haghpanahi,et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network , 2019, Nature Medicine.
[8] D. Dey,et al. Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study. , 2018, JACC. Cardiovascular imaging.
[9] Daniel Rueckert,et al. Deep learning cardiac motion analysis for human survival prediction , 2018, Nature Machine Intelligence.
[10] Kipp W. Johnson,et al. Big data, artificial intelligence, and cardiovascular precision medicine , 2018, Expert Review of Precision Medicine and Drug Development.
[11] Susanna Price,et al. 2018 ESC Guidelines for the management of cardiovascular diseases during pregnancy. , 2018, European heart journal.
[12] Jeroen J. Bax,et al. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. , 2018, European heart journal.
[13] S. Bangalore,et al. Future Direction for Using Artificial Intelligence to Predict and Manage Hypertension , 2018, Current Hypertension Reports.
[14] Henggui Zhang,et al. VoxelAtlasGAN: 3D Left Ventricle Segmentation on Echocardiography with Atlas Guided Generation and Voxel-to-voxel Discrimination , 2018, MICCAI.
[15] Kipp W. Johnson,et al. Artificial Intelligence in Cardiology. , 2018, Journal of the American College of Cardiology.
[16] B. Lindsay,et al. Smartwatch Algorithm for Automated Detection of Atrial Fibrillation. , 2018, Journal of the American College of Cardiology.
[17] Mike Voets,et al. Deep Learning: From Data Extraction to Large-Scale Analysis , 2018 .
[18] N. Sohoni,et al. Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch , 2018, JAMA cardiology.
[19] W. Tong,et al. Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles , 2018, Scientific Reports.
[20] Henggui Zhang,et al. Multi-Views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images , 2018, IEEE Transactions on Biomedical Engineering.
[21] Andrew Janowczyk,et al. A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue , 2018, PloS one.
[22] Ramy Arnaout,et al. Fast and accurate view classification of echocardiograms using deep learning , 2018, npj Digital Medicine.
[23] Pablo Laguna,et al. Distinct ECG Phenotypes Identified in Hypertrophic Cardiomyopathy Using Machine Learning Associate With Arrhythmic Risk Markers , 2018, Front. Physiol..
[24] Konstantinos N. Plataniotis,et al. Brain Tumor Type Classification via Capsule Networks , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).
[25] Mattias Ohlsson,et al. Improving prediction of heart transplantation outcome using deep learning techniques , 2018, Scientific Reports.
[26] Brett K. Beaulieu-Jones,et al. Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis , 2017, bioRxiv.
[27] Newton Howard,et al. Deep clustering of longitudinal data , 2018, ArXiv.
[28] Jascha Sohl-Dickstein,et al. Adversarial Examples that Fool both Computer Vision and Time-Limited Humans , 2018, NeurIPS.
[29] Henggui Zhang,et al. Detecting atrial fibrillation by deep convolutional neural networks , 2018, Comput. Biol. Medicine.
[30] Pan He,et al. Adversarial Examples: Attacks and Defenses for Deep Learning , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[31] Andrew H. Beck,et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer , 2017, JAMA.
[32] Max A. Viergever,et al. Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis , 2017, Medical Image Anal..
[33] T. Pyrkov,et al. Extracting biological age from biomedical data via deep learning: too much of a good thing? , 2017, bioRxiv.
[34] D. Singer,et al. Association of of Atrial Fibrillation Clinical Phenotypes With Treatment Patterns and Outcomes: A Multicenter Registry Study , 2017, JAMA cardiology.
[35] Pieter Abbeel,et al. Meta Learning Shared Hierarchies , 2017, ICLR.
[36] Geoffrey E. Hinton,et al. Dynamic Routing Between Capsules , 2017, NIPS.
[37] Ben Glocker,et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks , 2017, Journal of Cardiovascular Magnetic Resonance.
[38] C. Krittanawong. Future Physicians in the Era of Precision Cardiovascular Medicine , 2017, Circulation.
[39] Marcin Andrychowicz,et al. Sim-to-Real Transfer of Robotic Control with Dynamics Randomization , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[40] P. Abbeel,et al. Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments , 2017, ICLR.
[41] B Pyakillya,et al. Deep Learning for ECG Classification , 2017 .
[42] C. Krittanawong,et al. Identifying Genotypes and Phenotypes of Cardiovascular Diseases Using Big Data Analytics. , 2017, JAMA cardiology.
[43] Tathagato Rai Dastidar,et al. Analyzing Microscopic Images of Peripheral Blood Smear Using Deep Learning , 2017, DLMIA/ML-CDS@MICCAI.
[44] Yujian Li,et al. Deep neural mapping support vector machines , 2017, Neural Networks.
[45] Gregory S. Corrado,et al. Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning , 2017, ArXiv.
[46] Randal S. Olson,et al. Data-driven advice for applying machine learning to bioinformatics problems , 2017, PSB.
[47] Jaime S. Cardoso,et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support , 2017, Lecture Notes in Computer Science.
[48] Kumar Sricharan,et al. Recognizing Abnormal Heart Sounds Using Deep Learning , 2017, KHD@IJCAI.
[49] Wei Li,et al. A fused deep learning architecture for viewpoint classification of echocardiography , 2017, Inf. Fusion.
[50] C. Krittanawong,et al. Artificial Intelligence in Precision Cardiovascular Medicine. , 2017, Journal of the American College of Cardiology.
[51] Khalid Ashraf,et al. Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks , 2017, ArXiv.
[52] Hao Chen,et al. MagNet: A Two-Pronged Defense against Adversarial Examples , 2017, CCS.
[53] Konstantinos Kamnitsas,et al. Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation , 2017, IEEE Transactions on Medical Imaging.
[54] Kevin Miller,et al. Forward Thinking: Building Deep Random Forests , 2017, ArXiv.
[55] Felix Lau,et al. FastVentricle: Cardiac Segmentation with ENet , 2017, FIMH.
[56] Sebastian Thrun,et al. Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.
[57] T. Hastie,et al. Accuracy in Wrist-Worn, Sensor-Based Measurements of Heart Rate and Energy Expenditure in a Diverse Cohort , 2016, bioRxiv.
[58] Subhashini Venugopalan,et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.
[59] Purang Abolmaesumi,et al. Abstract 17562: Automatic Quality Assessment of Echo Apical 4-chamber Images Using Computer Deep Learning , 2016 .
[60] J. Karch. A machine learning perspective on repeated measures , 2016 .
[61] P. Kirchhof,et al. 2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS. , 2016, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.
[62] Konstantinos Kamnitsas,et al. Multi-input Cardiac Image Super-Resolution Using Convolutional Neural Networks , 2016, MICCAI.
[63] P. Kirchhof,et al. 2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS. , 2016, European heart journal.
[64] Jimeng Sun,et al. Using recurrent neural network models for early detection of heart failure onset , 2016, J. Am. Medical Informatics Assoc..
[65] Carlos Guestrin,et al. Model-Agnostic Interpretability of Machine Learning , 2016, ArXiv.
[66] Josef Urban,et al. DeepMath - Deep Sequence Models for Premise Selection , 2016, NIPS.
[67] Konrad P. Körding,et al. Toward an Integration of Deep Learning and Neuroscience , 2016, bioRxiv.
[68] M. Motwani,et al. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis , 2016, European heart journal.
[69] Chen Wang,et al. Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography , 2016, IEEE Transactions on Medical Imaging.
[70] Sergey Ioffe,et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.
[71] Ananthram Swami,et al. Practical Black-Box Attacks against Machine Learning , 2016, AsiaCCS.
[72] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[73] Michael J Ackerman,et al. Association of Arrhythmia-Related Genetic Variants With Phenotypes Documented in Electronic Medical Records. , 2016, JAMA.
[74] Hamid Jafarkhani,et al. A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI , 2015, Medical Image Anal..
[75] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[76] Marcus Schreckenberg,et al. Fully Automated Versus Standard Tracking of Left Ventricular Ejection Fraction and Longitudinal Strain: The FAST-EFs Multicenter Study. , 2015, Journal of the American College of Cardiology.
[77] Marc G. Bellemare,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[78] Rahul C. Deo,et al. Phenomapping for Novel Classification of Heart Failure With Preserved Ejection Fraction , 2015, Circulation.
[79] Gary S Collins,et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration , 2015, Annals of Internal Medicine.
[80] Daniel Rueckert,et al. Endocardial 3D Ultrasound Segmentation using Autocontext Random Forests , 2014, The MIDAS Journal.
[81] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[82] Mary Beth Terry,et al. Assessing the goodness of fit of personal risk models , 2014, Statistics in medicine.
[83] M. Kattan,et al. Calibration plots for risk prediction models in the presence of competing risks , 2014, Statistics in medicine.
[84] Yvonne Vergouwe,et al. Towards better clinical prediction models: seven steps for development and an ABCD for validation. , 2014, European heart journal.
[85] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[86] Ofer Harel,et al. Addressing Missing Data Mechanism Uncertainty using Multiple-Model Multiple Imputation: Application to a Longitudinal Clinical Trial. , 2012, The annals of applied statistics.
[87] Mike Kirby,et al. ESC Guidelines on the Management of Cardiovascular Diseases During Pregnancy , 2012 .
[88] Helmut Baumgartner,et al. ESC Guidelines for the management of grown-up congenital heart disease (new version 2010). , 2010, European heart journal.
[89] R. Pallàs-Areny,et al. Heart rate detection from single-foot plantar bioimpedance measurements in a weighing scale , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.
[90] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[91] Remco C. Veltkamp,et al. An Ensemble of Deep Support Vector Machines for Image Categorization , 2009, 2009 International Conference of Soft Computing and Pattern Recognition.
[92] David J. Hand,et al. Measuring classifier performance: a coherent alternative to the area under the ROC curve , 2009, Machine Learning.
[93] Martin Jansche,et al. Maximum Expected F-Measure Training of Logistic Regression Models , 2005, HLT.
[94] Raymond J. Mooney,et al. Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing , 2005 .
[95] Guy Carrault,et al. Atrial activity enhancement by Wiener filtering using an artificial neural network , 2001, IEEE Transactions on Biomedical Engineering.
[96] J. Habbema,et al. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. , 2001, Journal of clinical epidemiology.
[97] Vladimir Cherkassky,et al. The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.
[98] O. Pahlm,et al. Agreement between artificial neural networks and experienced electrocardiographer on electrocardiographic diagnosis of healed myocardial infarction. , 1996, Journal of the American College of Cardiology.
[99] Andrew W. Moore,et al. Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..
[100] W R Webber,et al. Practical detection of epileptiform discharges (EDs) in the EEG using an artificial neural network: a comparison of raw and parameterized EEG data. , 1994, Electroencephalography and clinical neurophysiology.
[101] J. Vos,et al. Computer-simulated neural networks: an appropriate model for motor development? , 1993, Early human development.
[102] Geoffrey E. Hinton. A Parallel Computation that Assigns Canonical Object-Based Frames of Reference , 1981, IJCAI.
[103] D. Rubin. INFERENCE AND MISSING DATA , 1975 .
[104] C. Krittanawong,et al. Crowdfunding for cardiovascular research. , 2018, International journal of cardiology.
[105] Qiao Li,et al. A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology , 2017, 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).
[106] Haifeng Liu,et al. Using Machine Learning Models to Predict In-Hospital Mortality for ST-Elevation Myocardial Infarction Patients , 2017, MedInfo.
[107] W. Ouwerkerk. A systems biology study to tailored treatment in chronic heart failure , 2017 .
[108] P. Ponikowski,et al. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure , 2016 .
[109] Survi Kyal,et al. Detection of atrial fibrillation using contactless facial video monitoring. , 2015, Heart rhythm.
[110] Danilo Samuel Jodas,et al. Comparing Support Vector Machines and Artificial Neural Networks in the Recognition Of Steering Angle for Driving of Mobile Robots Through Paths in Plantations , 2013, ICCS.