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pro vyhledávání: '"A. Durrleman"'
Introduction: Heterogeneity of the progression of neurodegenerative diseases is one of the main challenges faced in developing effective therapies. With the increasing number of large clinical databases, disease progression models have led to a bette
Externí odkaz:
http://arxiv.org/abs/2401.17249
Autor:
Franziska Baumeister, Pauline Wolfer, Sümeyra Sahbaz, Nicola Rudelli, Marine Capallera, Moritz M. Daum, Andrea C. Samson, Grace Corrigan, Letitia Naigles, Stephanie Durrleman
Publikováno v:
Frontiers in Developmental Psychology, Vol 2 (2024)
This study introduces a novel linguistically simple, tablet-based, behavioral Theory of Mind (ToM) measure, designed for neurotypical (NT) and autistic children aged 4–10 years. A synthesis of five comprehensive reviews of existing ToM measures rev
Externí odkaz:
https://doaj.org/article/fd04c766a27b475697a4c783088ff3f7
Publikováno v:
Frontiers in Communication, Vol 9 (2024)
While children with typical language development may capitalize on general language skills to grasp the content of others’ minds, those with challenges in mind-reading could rather rely more specifically on complementation structures. However, most
Externí odkaz:
https://doaj.org/article/2aa8964832a84527985ba8b3bac64874
Autor:
Nguyen, Ai-Tien, Cotteret, Camille, Gins, Clarisse, Sarda, Eugénie, Durrleman, Chloé, Mesples, Bettina, Bustamante, Jacinta, Fayard, Claire, Cisternino, Salvatore, Desguerre, Isabelle, Aubart, Mélodie
Publikováno v:
In Pediatric Neurology July 2024 156:79-84
Autor:
Routier, Alexandre, Burgos, Ninon, Díaz, Mauricio, Bacci, Michael, Bottani, Simona, El-Rifai, Omar, Fontanella, Sabrina, Gori, Pietro, Guillon, Jérémy, Guyot, Alexis, Hassanaly, Ravi, Jacquemont, Thomas, Lu, Pascal, Marcoux, Arnaud, Moreau, Tristan, Samper-González, Jorge, Teichmann, Marc, Thibeau--Sutre, Elina, Vaillant, Ghislain, Wen, Junhao, Wild, Adam, Habert, Marie-Odile, Durrleman, Stanley, Colliot, Olivier
We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to i) spend less time on data management and processing, ii) perform repr
Externí odkaz:
http://arxiv.org/abs/2107.10256
Autor:
Goparaju, Anupama, Bone, Alexandre, Hu, Nan, Henninger, Heath B., Anderson, Andrew E., Durrleman, Stanley, Jacxsens, Matthijs, Morris, Alan, Csecs, Ibolya, Marrouche, Nassir, Elhabian, Shireen Y.
Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes. Technological advancements of in vivo imaging have led to the development of op
Externí odkaz:
http://arxiv.org/abs/2009.02878
Publikováno v:
SN Computer Science, Springer, 2021, 2 (466), \&\#x27E8;10.1007/s42979-021-00865-5\&\#x27E9
Conditional correlation networks, within Gaussian Graphical Models (GGM), are widely used to describe the direct interactions between the components of a random vector. In the case of an unlabelled Heterogeneous population, Expectation Maximisation (
Externí odkaz:
http://arxiv.org/abs/2006.11094
Publikováno v:
Algorithms, MDPI, 2022, Stochastic Algorithms and Their Applications, 15 (3), pp.78
The Expectation Maximisation (EM) algorithm is widely used to optimise non-convex likelihood functions with latent variables. Many authors modified its simple design to fit more specific situations. For instance, the Expectation (E) step has been rep
Externí odkaz:
http://arxiv.org/abs/2003.10126
Autor:
Lartigue, Thomas, Bottani, Simona, Baron, Stephanie, Colliot, Olivier, Durrleman, Stanley, Allassonnière, Stéphanie
Gaussian Graphical Models (GGM) are often used to describe the conditional correlations between the components of a random vector. In this article, we compare two families of GGM inference methods: nodewise edge selection and penalised likelihood max
Externí odkaz:
http://arxiv.org/abs/2003.05169
Autor:
Marinescu, Razvan V., Oxtoby, Neil P., Young, Alexandra L., Bron, Esther E., Toga, Arthur W., Weiner, Michael W., Barkhof, Frederik, Fox, Nick C., Eshaghi, Arman, Toni, Tina, Salaterski, Marcin, Lunina, Veronika, Ansart, Manon, Durrleman, Stanley, Lu, Pascal, Iddi, Samuel, Li, Dan, Thompson, Wesley K., Donohue, Michael C., Nahon, Aviv, Levy, Yarden, Halbersberg, Dan, Cohen, Mariya, Liao, Huiling, Li, Tengfei, Yu, Kaixian, Zhu, Hongtu, Tamez-Pena, Jose G., Ismail, Aya, Wood, Timothy, Bravo, Hector Corrada, Nguyen, Minh, Sun, Nanbo, Feng, Jiashi, Yeo, B. T. Thomas, Chen, Gang, Qi, Ke, Chen, Shiyang, Qiu, Deqiang, Buciuman, Ionut, Kelner, Alex, Pop, Raluca, Rimocea, Denisa, Ghazi, Mostafa M., Nielsen, Mads, Ourselin, Sebastien, Sorensen, Lauge, Venkatraghavan, Vikram, Liu, Keli, Rabe, Christina, Manser, Paul, Hill, Steven M., Howlett, James, Huang, Zhiyue, Kiddle, Steven, Mukherjee, Sach, Rouanet, Anais, Taschler, Bernd, Tom, Brian D. M., White, Simon R., Faux, Noel, Sedai, Suman, Oriol, Javier de Velasco, Clemente, Edgar E. V., Estrada, Karol, Aksman, Leon, Altmann, Andre, Stonnington, Cynthia M., Wang, Yalin, Wu, Jianfeng, Devadas, Vivek, Fourrier, Clementine, Raket, Lars Lau, Sotiras, Aristeidis, Erus, Guray, Doshi, Jimit, Davatzikos, Christos, Vogel, Jacob, Doyle, Andrew, Tam, Angela, Diaz-Papkovich, Alex, Jammeh, Emmanuel, Koval, Igor, Moore, Paul, Lyons, Terry J., Gallacher, John, Tohka, Jussi, Ciszek, Robert, Jedynak, Bruno, Pandya, Kruti, Bilgel, Murat, Engels, William, Cole, Joseph, Golland, Polina, Klein, Stefan, Alexander, Daniel C.
Publikováno v:
Machine Learning for Biomedical Imaging (MELBA), Dec 2021
We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk
Externí odkaz:
http://arxiv.org/abs/2002.03419