Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Christopher B. Kendall"'
Autor:
Sahithi Sharma, Anand Reddy Maligireddy, Minako Katayama, Christopher B. Kendall, Hari P. Chaliki
Publikováno v:
CASE. 7:125-128
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::11065b9ab7a3f356b66f3ba9bb831294
https://doi.org/10.1016/j.case.2022.11.009
https://doi.org/10.1016/j.case.2022.11.009
Autor:
Ruibin Feng, Christopher B. Kendall, R. Todd Hurst, Jianming Liang, Jae Y. Shin, Zongwei Zhou
Publikováno v:
Journal of Digital Imaging. 32:290-299
Cardiovascular disease (CVD) is the number one killer in the USA, yet it is largely preventable (World Health Organization 2011). To prevent CVD, carotid intima-media thickness (CIMT) imaging, a noninvasive ultrasonography method, has proven to be cl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6b9054c002d9599e353ddc4129178ef4
https://doi.org/10.1007/s10278-018-0143-2
https://doi.org/10.1007/s10278-018-0143-2
Autor:
Patrick A. DeValeria, Christopher B. Kendall, Stephen S. Cha, John R. Daniels, Marek Belohlavek, Louis A. Lanza, Hari P. Chaliki, F. David Fortuin, John P. Sweeney, Prasad M. Panse, Minako Katayama
Publikováno v:
Journal of Ultrasound in Medicine. 37:217-224
Objectives Left ventricular (LV) septal hypertrophy in aortic stenosis raises diagnostic and therapeutic questions. However, the etiology and clinical consequences of this finding have not been well studied. The aim of this study was to perform a mor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::22f5a16d505f18412900cbe3f35af22e
https://doi.org/10.1002/jum.14320
https://doi.org/10.1002/jum.14320
Publikováno v:
CVPR
Cardiovascular disease (CVD) is the leading cause of mortality yet largely preventable, but the key to prevention is to identify at-risk individuals before adverse events. For predicting individual CVD risk, carotid intima-media thickness (CIMT), a n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5091aa4cd3218135fd0cc736d04cc0fd
http://arxiv.org/abs/1706.00719
http://arxiv.org/abs/1706.00719
Autor:
Minako, Katayama, Prasad M, Panse, Christopher B, Kendall, John R, Daniels, Stephen S, Cha, F David, Fortuin, John P, Sweeney, Patrick A, DeValeria, Louis A, Lanza, Marek, Belohlavek, Hari P, Chaliki
Publikováno v:
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine. 37(1)
Left ventricular (LV) septal hypertrophy in aortic stenosis raises diagnostic and therapeutic questions. However, the etiology and clinical consequences of this finding have not been well studied. The aim of this study was to perform a morphologic ev
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::ad5559ccfd95698811314acf319db93f
https://pubmed.ncbi.nlm.nih.gov/28741721
https://pubmed.ncbi.nlm.nih.gov/28741721
Publikováno v:
Deep Learning for Medical Image Analysis
Cardiovascular disease (CVD) is the leading cause of death in the United States, yet it is largely preventable. But a critical part of prevention is identification of at-risk persons before adverse events. For predicting individual CVD risk, carotid
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a946584e239466cd8d7ceb5fedfbf33b
https://doi.org/10.1016/b978-0-12-810408-8.00007-9
https://doi.org/10.1016/b978-0-12-810408-8.00007-9
Autor:
R. Todd Hurst, Christopher B. Kendall, Michael B. Gotway, Jae Y. Shin, Suryakanth R. Gurudu, Nima Tajbakhsh, Jianming Liang
Publikováno v:
Deep Learning and Convolutional Neural Networks for Medical Image Computing ISBN: 9783319429984
Deep Learning and Convolutional Neural Networks for Medical Image Computing
Deep Learning and Convolutional Neural Networks for Medical Image Computing
This study aims to address two central questions. First, are fine-tuned convolutional neural networks (CNNs) necessary for medical imaging applications? In response, we considered four medical vision tasks from three different medical imaging modalit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::892a821392311fb5c8d14d400a45bffa
https://doi.org/10.1007/978-3-319-42999-1_11
https://doi.org/10.1007/978-3-319-42999-1_11
Autor:
R. Venkatesh Babu, Yaniv Bar, Joseph Blair, Andrew P. Bradley, Gustavo Carneiro, Hao Chen, Erik B. Dam, Idit Diamant, Qi Dou, Michael D. Feldman, Renee Frank, Yaozong Gao, Bogdan Georgescu, Ofer Geva, Florin C. Ghesu, Hayit Greenspan, Yanrong Guo, Pheng-Ann Heng, Joachim Hornegger, R. Todd Hurst, Christian Igel, Vamsi K. Ithapu, Andrew Janowczyk, Sterling C. Johnson, Michiel Kallenberg, Christopher B. Kendall, Minjeong Kim, Eli Konen, Srinivas S.S. Kruthiventi, Jianming Liang, Rui Liao, Sivan Lieberman, Le Lu, Anant Madabhushi, Kenneth B. Margulies, Dimitris Metaxas, Shun Miao, Vincent C.T. Mok, Konda R. Mopuri, Jacinto Nascimento, Hien Van Nguyen, Mads Nielsen, Jeffrey J. Nirschl, Akshay Pai, Eliot G. Peyster, Nikita Prabhu, Jing Qin, Ravi K. Sarvadevabhatla, Dinggang Shen, Lin Shi, Hoo-Chang Shin, Jae Y. Shin, Vikas Singh, Stefan Sommer, Suraj Srinivas, Heung-Il Suk, Ronald M. Summers, Nima Tajbakhsh, Yuan-Ching Teng, Raviteja Vemulapalli, Defeng Wang, Jane Z. Wang, Shaoyu Wang, Lior Wolf, Guorong Wu, Yuanpu Xie, Fuyong Xing, Zhennan Yan, Lin Yang, Lequan Yu, Yiqiang Zhan, Shaoting Zhang, Lei Zhao, S. Kevin Zhou, Xiang Sean Zhou, Gali Zimmerman
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::08515dc6c341cb0e65d35266883668cf
https://doi.org/10.1016/b978-0-12-810408-8.00029-8
https://doi.org/10.1016/b978-0-12-810408-8.00029-8
Autor:
Christopher B. Kendall, Nima Tajbakhsh, Michael B. Gotway, Jianming Liang, Jae Y. Shin, Suryakanth R. Gurudu, R. Todd Hurst
Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that ha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::14e2fdad4889fd850cdca91ef497f230
Autor:
Christopher B. Kendall, Fayaz A. Hakim, Roger L. Click, Anil Pandit, Aashrayata Aryal Pandit, Jorge R. Alegria, Madan Raj Aryal
Publikováno v:
Echocardiography. 31:234-240
The pulmonary valve is the least affected site for valvular papillary fibroelastoma. With increasing use of routine echocardiography and other modalities of imaging, pulmonary valve papillary fibroelastomas (PVPFE) are being recognized more frequentl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5a62200b47e25798a0e393fe9368c6bf
https://doi.org/10.1111/echo.12388
https://doi.org/10.1111/echo.12388