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pro vyhledávání: '"Marinescu, Razvan V"'
BrainPainter is a software for the 3D visualization of human brain structures; it generates colored brain images using user-defined biomarker data for each brain region. However, BrainPainter is only able to generate human brain images. In this paper
Externí odkaz:
http://arxiv.org/abs/2103.14696
Publikováno v:
NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications
Machine learning models are commonly trained end-to-end and in a supervised setting, using paired (input, output) data. Examples include recent super-resolution methods that train on pairs of (low-resolution, high-resolution) images. However, these e
Externí odkaz:
http://arxiv.org/abs/2012.04567
Autor:
Marinescu, Razvan V.
In order to find effective treatments for Alzheimer's disease (AD), we need to identify subjects at risk of AD as early as possible. To this end, recently developed disease progression models can be used to perform early diagnosis, as well as predict
Externí odkaz:
http://arxiv.org/abs/2003.04805
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
Autor:
Marinescu, Razvan V., Oxtoby, Neil P., Young, Alexandra L., Bron, Esther E., Toga, Arthur W., Weiner, Michael W., Barkhof, Frederik, Fox, Nick C., Golland, Polina, Klein, Stefan, Alexander, Daniel C.
Publikováno v:
MICCAI Multimodal Brain Image Analysis Workshop, 2019
The TADPOLE Challenge compares the performance of algorithms at predicting the future evolution of individuals at risk of Alzheimer's disease. TADPOLE Challenge participants train their models and algorithms on historical data from the Alzheimer's Di
Externí odkaz:
http://arxiv.org/abs/2001.09016
We present BrainPainter, a software that automatically generates images of highlighted brain structures given a list of numbers corresponding to the output colours of each region. Compared to existing visualisation software (i.e. Freesurfer, SPM, 3D
Externí odkaz:
http://arxiv.org/abs/1905.08627
Autor:
Marinescu, Razvan V., Eshaghi, Arman, Lorenzi, Marco, Young, Alexandra L., Oxtoby, Neil P., Garbarino, Sara, Crutch, Sebastian J., Alexander, Daniel C.
Publikováno v:
NeuroImage, Volume 192, 15 May 2019, Pages 166-177
Here we present DIVE: Data-driven Inference of Vertexwise Evolution. DIVE is an image-based disease progression model with single-vertex resolution, designed to reconstruct long-term patterns of brain pathology from short-term longitudinal data sets.
Externí odkaz:
http://arxiv.org/abs/1901.03553
Autor:
Marinescu, Razvan V., Lorenzi, Marco, Blumberg, Stefano B., Young, Alexandra L., Morell, Pere P., Oxtoby, Neil P., Eshaghi, Arman, Yong, Keir X., Crutch, Sebastian J., Golland, Polina, Alexander, Daniel C.
Publikováno v:
Medical Image Computing and Computer Assisted Intervention 2019
We introduce Disease Knowledge Transfer (DKT), a novel technique for transferring biomarker information between related neurodegenerative diseases. DKT infers robust multimodal biomarker trajectories in rare neurodegenerative diseases even when only
Externí odkaz:
http://arxiv.org/abs/1901.03517
Autor:
Marinescu, Razvan V., Oxtoby, Neil P., Young, Alexandra L., Bron, Esther E., Toga, Arthur W., Weiner, Michael W., Barkhof, Frederik, Fox, Nick C., Klein, Stefan, Alexander, Daniel C., Consortium, the EuroPOND
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge compares the performance of algorithms at predicting future evolution of individuals at risk of Alzheimer's disease. TADPOLE Challenge participants train their models an
Externí odkaz:
http://arxiv.org/abs/1805.03909
Autor:
Marinescu, Razvan V., Oxtoby, Neil P., Young, Alexandra L., Bron, Esther E., Toga, Arthur W., Weiner, Michael W., Frederik Barkhof, Fox, Nick C., Arman Eshaghi, Tina Toni, Marcin Salaterski, Veronika Lunina, Manon Ansart, Stanley Durrleman, Pascal Lu, Samuel Iddi, Dan Li, Thompson, Wesley K., Donohue, Michael C., Aviv Nahon, Yarden Levy, Dan Halbersberg, Mariya Cohen, Huiling Liao, Tengfei Li, Kaixian Yu, Hongtu Zhu, Tamez-Peña, José G., Aya Ismail, Timothy Wood, Hector Corrada Bravo, Minh Nguyen, Nanbo Sun, Jiashi Feng, Thomas Yeo, B. T., Gang Chen, Ke Qi, Shiyang Chen, Deqiang Qiu, Ionut Buciuman, Alex Kelner, Raluca Pop, Denisa Rimocea, Ghazi, Mostafa M., Mads Nielsen, Sebastien Ourselin, Lauge Sørensen, Vikram Venkatraghavan, Keli Liu, Christina Rabe, Paul Manser, Hill, Steven M., James Howlett, Zhiyue Huang, Steven Kiddle, Sach Mukherjee, Anaïs Rouanet, Bernd Taschler, Tom, Brian D. M., White, Simon R., Noel Faux, Suman Sedai, Javier de Velasco Oriol, Clemente, Edgar E. V., Karol Estrada, Leon Aksman, Andre Altmann, Stonnington, Cynthia M., Yalin Wang, Jianfeng Wu, Vivek Devadas, Clementine Fourrier, Lars Lau Raket, Aristeidis Sotiras, Guray Erus, Jimit Doshi, Christos Davatzikos, Jacob Vogel, Andrew Doyle, Angela Tam, Alex Diaz-Papkovich, Emmanuel Jammeh, Igor Koval, Paul Moore, Lyons, Terry J., John Gallacher, Jussi Tohka, Robert Ciszek, Bruno Jedynak, Kruti Pandya, Murat Bilgel, William Engels, Joseph Cole, Polina Golland, Stefan Klein, Alexander, Daniel C.
Publikováno v:
Machine Learning for Biomedical Imaging, 1
EUR Research Portal
EUR Research Portal
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ab0e7be456a19285c3315e5245732697
https://pure.eur.nl/en/publications/d50d3c2a-cf7b-42d1-84b4-d4faacfe53e6
https://pure.eur.nl/en/publications/d50d3c2a-cf7b-42d1-84b4-d4faacfe53e6