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pro vyhledávání: '"Leahy, Richard M."'
Autor:
Payette, Kelly, Steger, Céline, Licandro, Roxane, de Dumast, Priscille, Li, Hongwei Bran, Barkovich, Matthew, Li, Liu, Dannecker, Maik, Chen, Chen, Ouyang, Cheng, McConnell, Niccolò, Miron, Alina, Li, Yongmin, Uus, Alena, Grigorescu, Irina, Gilliland, Paula Ramirez, Siddiquee, Md Mahfuzur Rahman, Xu, Daguang, Myronenko, Andriy, Wang, Haoyu, Huang, Ziyan, Ye, Jin, Alenyà, Mireia, Comte, Valentin, Camara, Oscar, Masson, Jean-Baptiste, Nilsson, Astrid, Godard, Charlotte, Mazher, Moona, Qayyum, Abdul, Gao, Yibo, Zhou, Hangqi, Gao, Shangqi, Fu, Jia, Dong, Guiming, Wang, Guotai, Rieu, ZunHyan, Yang, HyeonSik, Lee, Minwoo, Płotka, Szymon, Grzeszczyk, Michal K., Sitek, Arkadiusz, Daza, Luisa Vargas, Usma, Santiago, Arbelaez, Pablo, Lu, Wenying, Zhang, Wenhao, Liang, Jing, Valabregue, Romain, Joshi, Anand A., Nayak, Krishna N., Leahy, Richard M., Wilhelmi, Luca, Dändliker, Aline, Ji, Hui, Gennari, Antonio G., Jakovčić, Anton, Klaić, Melita, Adžić, Ana, Marković, Pavel, Grabarić, Gracia, Kasprian, Gregor, Dovjak, Gregor, Rados, Milan, Vasung, Lana, Cuadra, Meritxell Bach, Jakab, Andras
Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and the Fetal Brain Tissue Annotation (FeTA) Challenge 2021 helped to establ
Externí odkaz:
http://arxiv.org/abs/2402.09463
Despite the impressive advancements achieved using deep-learning for functional brain activity analysis, the heterogeneity of functional patterns and scarcity of imaging data still pose challenges in tasks such as prediction of future onset of Post-T
Externí odkaz:
http://arxiv.org/abs/2312.14204
Autor:
Cui, Wenhui, Jeong, Woojae, Thölke, Philipp, Medani, Takfarinas, Jerbi, Karim, Joshi, Anand A., Leahy, Richard M.
To handle the scarcity and heterogeneity of electroencephalography (EEG) data for Brain-Computer Interface (BCI) tasks, and to harness the power of large publicly available data sets, we propose Neuro-GPT, a foundation model consisting of an EEG enco
Externí odkaz:
http://arxiv.org/abs/2311.03764
Despite the remarkable success achieved by graph convolutional networks for functional brain activity analysis, the heterogeneity of functional patterns and the scarcity of imaging data still pose challenges in many tasks. Transferring knowledge from
Externí odkaz:
http://arxiv.org/abs/2212.08217
Segmentation is one of the most important tasks in MRI medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis, head segmentation is commonly used for measuring and visualizing the
Externí odkaz:
http://arxiv.org/abs/2208.04941
The amount of manually labeled data is limited in medical applications, so semi-supervised learning and automatic labeling strategies can be an asset for training deep neural networks. However, the quality of the automatically generated labels can be
Externí odkaz:
http://arxiv.org/abs/2203.01524
Due to the spontaneous nature of resting-state fMRI (rs-fMRI) signals, cross-subject comparison and therefore, group studies of rs-fMRI are challenging. Most existing group comparison methods use features extracted from the fMRI time series, such as
Externí odkaz:
http://arxiv.org/abs/2012.06972
Autor:
Medani, Takfarinas, Garcia-Prieto, Juan, Tadel, Francois, Schrader, Sophie, Joshi, Anand, Engwer, Christian, Wolters, Carsten H., Mosher, John C., Leahy, Richard M.
Human brain activity generates scalp potentials (electroencephalography EEG), intracranial potentials (iEEG), and external magnetic fields (magnetoencephalography MEG), all capable of being recorded, often simultaneously, for use in research and clin
Externí odkaz:
http://arxiv.org/abs/2011.01292
Estimation of uncertainty in deep learning models is of vital importance, especially in medical imaging, where reliance on inference without taking into account uncertainty could lead to misdiagnosis. Recently, the probabilistic Variational AutoEncod
Externí odkaz:
http://arxiv.org/abs/2010.09042
We propose a robust variational autoencoder with $\beta$ divergence for tabular data (RTVAE) with mixed categorical and continuous features. Variational autoencoders (VAE) and their variations are popular frameworks for anomaly detection problems. Th
Externí odkaz:
http://arxiv.org/abs/2006.08204