Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Joshua V. Stough"'
Publikováno v:
Medical Imaging 2023: Image Processing.
Publikováno v:
ISBI
Bayesian Optimization (BO) is a well-studied hyperparameter tuning technique that is more efficient than grid search for high-cost, high-parameter machine learning problems. Echocardiography is a ubiquitous modality for evaluating heart structure and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd59884b4a5ac11ace07ce6bea8c3e36
http://arxiv.org/abs/2211.09888
http://arxiv.org/abs/2211.09888
Publikováno v:
Medical Imaging 2022: Image Processing.
Publikováno v:
Medical Imaging 2022: Image Processing.
Autor:
Alvaro E. Ulloa Cerna, Joshua V. Stough, Brandon K. Fornwalt, Seth Gazes, Joseph B. Leader, Christopher W. Good, Gargi Schneider, David M. Riviello, Sushravya Raghunath, H. Lester Kirchner, Allyson Haggerty, Linyuan Jing, Nathan M Sauers, Dustin N. Hartzel, Brendan J. Carry, Yirui Hu, Christopher M. Haggerty
Publikováno v:
JACC: Heart Failure. 8:578-587
Background Heart failure is a prevalent, costly disease for which new value-based payment models demand optimized population management strategies. Objectives This study sought to generate a strategy for managing populations of patients with heart fa
Autor:
Xiaoyan Zhang, Alvaro E. Ulloa Cerna, Joshua V. Stough, Yida Chen, Brendan J. Carry, Amro Alsaid, Sushravya Raghunath, David P. vanMaanen, Brandon K. Fornwalt, Christopher M. Haggerty
Use of machine learning for automated annotation of heart structures from echocardiographic videos is an active research area, but understanding of comparative, generalizable performance among models is lacking. This study aimed to 1) assess the gene
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a2fa3b075ed192166c621830ea6a11f0
https://doi.org/10.21203/rs.3.rs-1097714/v1
https://doi.org/10.21203/rs.3.rs-1097714/v1
Autor:
Xiaoyan, Zhang, Alvaro E Ulloa, Cerna, Joshua V, Stough, Yida, Chen, Brendan J, Carry, Amro, Alsaid, Sushravya, Raghunath, David P, vanMaanen, Brandon K, Fornwalt, Christopher M, Haggerty
Publikováno v:
The international journal of cardiovascular imaging.
Use of machine learning (ML) for automated annotation of heart structures from echocardiographic videos is an active research area, but understanding of comparative, generalizable performance among models is lacking. This study aimed to (1) assess th
Publikováno v:
Medical Imaging 2021: Image Processing.
Existing deep-learning methods achieve state-of-art segmentation of multiple heart substructures from 2D echocardiography videos, an important step in the diagnosis and management of cardiovascular disease. However, these methods generally perform fr
Autor:
Christopher M. Haggerty, Sushravya Raghunath, David P. vanMaanen, Brandon K. Fornwalt, Xiaoyan Zhang, Alvaro E. Ulloa Cerna, Joshua V. Stough
Publikováno v:
Circulation. 142
Introduction: The use of convolutional neural networks (CNN) to automatically segment the heart from echocardiography images has garnered recent attention, but generalizable performance segmenting multiple cardiac structures has not been demonstrated
Autor:
Joshua V. Stough, Xiaoyan Zhang, Brandon K. Fornwalt, Sushravya Raghunath, Christopher M. Haggerty, John M. Pfeifer
Publikováno v:
Medical Imaging: Image Processing
Segmentation of heart substructures in 2D echocardiography images is an important step in diagnosis and management of cardiovascular disease. Given the ubiquity of echocardiography in routine cardiology practice, the time-consuming nature of manual s