Zobrazeno 11 - 20
of 3 993
pro vyhledávání: '"Thompson, Paul P."'
Autor:
Gadewar, Shruti P., Nourollahimoghadam, Elnaz, Bhatt, Ravi R., Ramesh, Abhinaav, Javid, Shayan, Gari, Iyad Ba, Zhu, Alyssa H., Thomopoulos, Sophia, Thompson, Paul M., Jahanshad, Neda
Structural alterations of the midsagittal corpus callosum (midCC) have been associated with a wide range of brain disorders. The midCC is visible on most MRI contrasts and in many acquisitions with a limited field-of-view. Here, we present an automat
Externí odkaz:
http://arxiv.org/abs/2305.01107
Autor:
Wu, Jianfeng, Su, Yi, Chen, Yanxi, Zhu, Wenhui, Reiman, Eric M., Caselli, Richard J., Chen, Kewei, Thompson, Paul M., Wang, Junwen, Wang, Yalin
Background: Alzheimer's Disease (AD) is the most common type of age-related dementia, affecting 6.2 million people aged 65 or older according to CDC data. It is commonly agreed that discovering an effective AD diagnosis biomarker could have enormous
Externí odkaz:
http://arxiv.org/abs/2304.00134
Autor:
Dhinagar, Nikhil J., Santhalingam, Vignesh, Lawrence, Katherine E., Laltoo, Emily, Thompson, Paul M.
For machine learning applications in medical imaging, the availability of training data is often limited, which hampers the design of radiological classifiers for subtle conditions such as autism spectrum disorder (ASD). Transfer learning is one meth
Externí odkaz:
http://arxiv.org/abs/2303.08224
Neuroimaging of large populations is valuable to identify factors that promote or resist brain disease, and to assist diagnosis, subtyping, and prognosis. Data-driven models such as convolutional neural networks (CNNs) have increasingly been applied
Externí odkaz:
http://arxiv.org/abs/2303.08216
Autor:
Gupta, Umang, Chattopadhyay, Tamoghna, Dhinagar, Nikhil, Thompson, Paul M., Steeg, Greg Ver, Initiative, The Alzheimer's Disease Neuroimaging
Transfer learning has remarkably improved computer vision. These advances also promise improvements in neuroimaging, where training set sizes are often small. However, various difficulties arise in directly applying models pretrained on natural image
Externí odkaz:
http://arxiv.org/abs/2303.01491
Autor:
Dhinagar, Nikhil J., Owens-Walton, Conor, Laltoo, Emily, Boyle, Christina P., Chen, Yao-Liang, Cook, Philip, McMillan, Corey, Tsai, Chih-Chien, Wang, J-J, Wu, Yih-Ru, van der Werf, Ysbrand, Thompson, Paul M.
There is great interest in developing radiological classifiers for diagnosis, staging, and predictive modeling in progressive diseases such as Parkinson's disease (PD), a neurodegenerative disease that is difficult to detect in its early stages. Here
Externí odkaz:
http://arxiv.org/abs/2302.13631
Autor:
Wu, Jianfeng, Su, Yi, Zhu, Wenhui, Mallak, Negar Jalili, Lepore, Natasha, Reiman, Eric M., Caselli, Richard J., Thompson, Paul M., Chen, Kewei, Wang, Yalin
Background: Beta-amyloid (A$\beta$) plaques and tau protein tangles in the brain are the defining 'A' and 'T' hallmarks of Alzheimer's disease (AD), and together with structural atrophy detectable on brain magnetic resonance imaging (MRI) scans as on
Externí odkaz:
http://arxiv.org/abs/2211.05235
Autor:
Stripelis, Dimitris, Gupta, Umang, Dhinagar, Nikhil, Steeg, Greg Ver, Thompson, Paul, Ambite, José Luis
Federated training of large deep neural networks can often be restrictive due to the increasing costs of communicating the updates with increasing model sizes. Various model pruning techniques have been designed in centralized settings to reduce infe
Externí odkaz:
http://arxiv.org/abs/2208.11669
Autor:
Belov, Vladimir, Erwin-Grabner, Tracy, Gonul, Ali Saffet, Amod, Alyssa R., Ojha, Amar, Aleman, Andre, Dols, Annemiek, Scharntee, Anouk, Uyar-Demir, Aslihan, Harrison, Ben J, Irungu, Benson M., Besteher, Bianca, Klimes-Dougan, Bonnie, Penninx, Brenda W. J. H., Mueller, Bryon A., Zarate, Carlos, Davey, Christopher G., Ching, Christopher R. K., Connolly, Colm G., Fu, Cynthia H. Y., Stein, Dan J., Dima, Danai, Linden, David E. J., Mehler, David M. A., Pomarol-Clotet, Edith, Pozzi, Elena, Melloni, Elisa, Benedetti, Francesco, MacMaster, Frank P., Grabe, Hans J., Völzke, Henry, Gotlib, Ian H., Soares, Jair C., Evans, Jennifer W., Sim, Kang, Wittfeld, Katharina, Cullen, Kathryn, Reneman, Liesbeth, Oudega, Mardien L., Wright, Margaret J., Portella, Maria J., Sacchet, Matthew D., Li, Meng, Aghajani, Moji, Wu, Mon-Ju, Jaworska, Natalia, Jahanshad, Neda, van der Wee, Nic J. A., Groenewold, Nynke, Hamilton, Paul J., Saemann, Philipp, Bülow, Robin, Poletti, Sara, Whittle, Sarah, Thomopoulos, Sophia I., van, Steven J. A., Werff, der, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Ho, Tiffany C., Yang, Tony T., Basgoze, Zeynep, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., Goya-Maldonado, Roberto
Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample
Externí odkaz:
http://arxiv.org/abs/2206.08122
Autor:
Stripelis, Dimitris, Gupta, Umang, Saleem, Hamza, Dhinagar, Nikhil, Ghai, Tanmay, Anastasiou, Rafael Chrysovalantis, Asghar, Armaghan, Steeg, Greg Ver, Ravi, Srivatsan, Naveed, Muhammad, Thompson, Paul M., Ambite, Jose Luis
The amount of biomedical data continues to grow rapidly. However, collecting data from multiple sites for joint analysis remains challenging due to security, privacy, and regulatory concerns. To overcome this challenge, we use Federated Learning, whi
Externí odkaz:
http://arxiv.org/abs/2205.05249