Zobrazeno 1 - 10
of 156
pro vyhledávání: '"Bosschieter P"'
Autor:
Peng, Wei, Xia, Tian, Ribeiro, Fabio De Sousa, Bosschieter, Tomas, Adeli, Ehsan, Zhao, Qingyu, Glocker, Ben, Pohl, Kilian M.
The number of samples in structural brain MRI studies is often too small to properly train deep learning models. Generative models show promise in addressing this issue by effectively learning the data distribution and generating high-fidelity MRI. H
Externí odkaz:
http://arxiv.org/abs/2409.05585
Autor:
Van Ness, Mike, Bosschieter, Tomas, Din, Natasha, Ambrosy, Andrew, Sandhu, Alexander, Udell, Madeleine
Survival analysis, or time-to-event analysis, is an important and widespread problem in healthcare research. Medical research has traditionally relied on Cox models for survival analysis, due to their simplicity and interpretability. Cox models assum
Externí odkaz:
http://arxiv.org/abs/2310.15472
Autor:
Bosschieter, Tomas M., Xu, Zifei, Lan, Hui, Lengerich, Benjamin J., Nori, Harsha, Painter, Ian, Souter, Vivienne, Caruana, Rich
Although most pregnancies result in a good outcome, complications are not uncommon and can be associated with serious implications for mothers and babies. Predictive modeling has the potential to improve outcomes through better understanding of risk
Externí odkaz:
http://arxiv.org/abs/2310.10203
Autor:
Peng, Wei, Bosschieter, Tomas, Ouyang, Jiahong, Paul, Robert, Adeli, Ehsan, Zhao, Qingyu, Pohl, Kilian M.
Generative AI models hold great potential in creating synthetic brain MRIs that advance neuroimaging studies by, for example, enriching data diversity. However, the mainstay of AI research only focuses on optimizing the visual quality (such as signal
Externí odkaz:
http://arxiv.org/abs/2310.04630
Publikováno v:
MICCAI 2023
As acquiring MRIs is expensive, neuroscience studies struggle to attain a sufficient number of them for properly training deep learning models. This challenge could be reduced by MRI synthesis, for which Generative Adversarial Networks (GANs) are pop
Externí odkaz:
http://arxiv.org/abs/2212.08034
Publikováno v:
SIGKDD 2023 Proceedings p5004 5015
Missing data is common in applied data science, particularly for tabular data sets found in healthcare, social sciences, and natural sciences. Most supervised learning methods only work on complete data, thus requiring preprocessing such as missing v
Externí odkaz:
http://arxiv.org/abs/2211.09259
Autor:
Bosschieter, Tomas M., Xu, Zifei, Lan, Hui, Lengerich, Benjamin J., Nori, Harsha, Sitcov, Kristin, Souter, Vivienne, Caruana, Rich
Most pregnancies and births result in a good outcome, but complications are not uncommon and when they do occur, they can be associated with serious implications for mothers and babies. Predictive modeling has the potential to improve outcomes throug
Externí odkaz:
http://arxiv.org/abs/2207.05322
Autor:
Fons Schipper, Angela Grassi, Marco Ross, Andreas Cerny, Peter Anderer, Lieke Hermans, Fokke van Meulen, Mickey Leentjens, Emily Schoustra, Pien Bosschieter, Ruud J. G. van Sloun, Sebastiaan Overeem, Pedro Fonseca
Publikováno v:
Sensors, Vol 24, Iss 17, p 5717 (2024)
Overnight sleep staging is an important part of the diagnosis of various sleep disorders. Polysomnography is the gold standard for sleep staging, but less-obtrusive sensing modalities are of emerging interest. Here, we developed and validated an algo
Externí odkaz:
https://doaj.org/article/2b949ab816014a0ca955c56c9a0f3abb
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Akademický článek
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