Zobrazeno 1 - 10
of 329
pro vyhledávání: '"Tong, Elizabeth"'
Autor:
O'Keefe, James B, Tong, Elizabeth J, Taylor Jr, Thomas H, O’Keefe, Ghazala A Datoo, Tong, David C
Publikováno v:
JMIR Public Health and Surveillance, Vol 7, Iss 4, p e25075 (2021)
BackgroundRisk assessment of patients with acute COVID-19 in a telemedicine context is not well described. In settings of large numbers of patients, a risk assessment tool may guide resource allocation not only for patient care but also for maximum h
Externí odkaz:
https://doaj.org/article/077e22f84abd46e6a38d3088ab50a178
The use of large language models (LLMs) in healthcare is gaining popularity, but their practicality and safety in clinical settings have not been thoroughly assessed. In high-stakes environments like medical settings, trust and safety are critical is
Externí odkaz:
http://arxiv.org/abs/2306.15887
Autor:
Niedermaier, Benedikt, Kou, Yao, Tong, Elizabeth, Eichinger, Monika, Klotz, Laura V., Eichhorn, Martin E., Muley, Thomas, Herth, Felix, Kauczor, Hans-Ulrich, Peter Heußel, Claus, Winter, Hauke
Publikováno v:
In Lung Cancer August 2024 194
Autor:
Auguste, Kurtis, Beni, Catherine, Braga, Bruno P., Buckley, Robert T., Chu, Jason, Durand, Rachelle, Floan, Gretchen M., Gonda, David D., Iyer, Rajiv R., Jamshidi, Ramin, Koral, Korgun, Kruk, Peter G., Linnau, Ken, Jason Liu, Chia-Shang, McNevin, Kathryn, O'Neill, Brent, Pandya, Samir, Polukoff, Natalya E., Prendergast, Connor, Prolo, Laura M., Rampton, John, Regner, Michael, Ronecker, Jennifer, Sabapaty, Akanksha, Sayama, Christine, Selesner, Leigh, Smith, Karch M., Stence, Nick, Thiessen, Jaclyn, Tong, Elizabeth, Vaughn, Jennifer A., Melhado, Caroline, Russell, Katie W., Acker, Shannon N., Padilla, Benjamin E., Lofberg, Katrine, Spurrier, Ryan G., Robinson, Bryce, Chao, Stephanie, Ignacio, Romeo C., Ryan, Mark, Jensen, Aaron R.
Publikováno v:
In Journal of Pediatric Surgery February 2024 59(2):326-330
Autor:
Talebi, Salmonn1 (AUTHOR), Tong, Elizabeth2 (AUTHOR), Li, Anna2 (AUTHOR), Yamin, Ghiam2 (AUTHOR), Zaharchuk, Greg2 (AUTHOR), Mofrad, Mohammad R. K.1 (AUTHOR) mofrad@berkeley.edu
Publikováno v:
BMC Medical Informatics & Decision Making. 2/7/2024, Vol. 24 Issue 1, p1-12. 12p.
We introduce a novel ensembling method, Random Bundle (RB), that improves performance for brain metastases segmentation. We create our ensemble by training each network on our dataset with 50% of our annotated lesions censored out. We also apply a lo
Externí odkaz:
http://arxiv.org/abs/2002.09809
Deep learning has proven to be an essential tool for medical image analysis. However, the need for accurately labeled input data, often requiring time- and labor-intensive annotation by experts, is a major limitation to the use of deep learning. One
Externí odkaz:
http://arxiv.org/abs/2001.09501
Autor:
Grøvik, Endre, Yi, Darvin, Iv, Michael, Tong, Elizabeth, Nilsen, Line Brennhaug, Latysheva, Anna, Saxhaug, Cathrine, Jacobsen, Kari Dolven, Helland, Åslaug, Emblem, Kyrre Eeg, Rubin, Daniel, Zaharchuk, Greg
The purpose was to assess the clinical value of a novel DropOut model for detecting and segmenting brain metastases, in which a neural network is trained on four distinct MRI sequences using an input dropout layer, thus simulating the scenario of mis
Externí odkaz:
http://arxiv.org/abs/1912.11966
Autor:
Yi, Darvin, Grøvik, Endre, Iv, Michael, Tong, Elizabeth, Emblem, Kyrre Eeg, Nilsen, Line Brennhaug, Saxhaug, Cathrine, Latysheva, Anna, Jacobsen, Kari Dolven, Helland, Åslaug, Zaharchuk, Greg, Rubin, Daniel
Magnetic resonance (MR) imaging is an essential diagnostic tool in clinical medicine. Recently, a variety of deep learning methods have been applied to segmentation tasks in medical images, with promising results for computer-aided diagnosis. For MR
Externí odkaz:
http://arxiv.org/abs/1912.08775
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