Zobrazeno 1 - 10
of 3 081
pro vyhledávání: '"Aertsen A"'
Autor:
Payette, Kelly, Li, Hongwei, de Dumast, Priscille, Licandro, Roxane, Ji, Hui, Siddiquee, Md Mahfuzur Rahman, Xu, Daguang, Myronenko, Andriy, Liu, Hao, Pei, Yuchen, Wang, Lisheng, Peng, Ying, Xie, Juanying, Zhang, Huiquan, Dong, Guiming, Fu, Hao, Wang, Guotai, Rieu, ZunHyan, Kim, Donghyeon, Kim, Hyun Gi, Karimi, Davood, Gholipour, Ali, Torres, Helena R., Oliveira, Bruno, Vilaça, João L., Lin, Yang, Avisdris, Netanell, Ben-Zvi, Ori, Bashat, Dafna Ben, Fidon, Lucas, Aertsen, Michael, Vercauteren, Tom, Sobotka, Daniel, Langs, Georg, Alenyà, Mireia, Villanueva, Maria Inmaculada, Camara, Oscar, Fadida, Bella Specktor, Joskowicz, Leo, Weibin, Liao, Yi, Lv, Xuesong, Li, Mazher, Moona, Qayyum, Abdul, Puig, Domenec, Kebiri, Hamza, Zhang, Zelin, Xu, Xinyi, Wu, Dan, Liao, KuanLun, Wu, YiXuan, Chen, JinTai, Xu, Yunzhi, Zhao, Li, Vasung, Lana, Menze, Bjoern, Cuadra, Meritxell Bach, Jakab, Andras
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in th
Externí odkaz:
http://arxiv.org/abs/2204.09573
Autor:
Fidon, Lucas, Aertsen, Michael, Kofler, Florian, Bink, Andrea, David, Anna L., Deprest, Thomas, Emam, Doaa, Guffens, Frédéric, Jakab, András, Kasprian, Gregor, Kienast, Patric, Melbourne, Andrew, Menze, Bjoern, Mufti, Nada, Pogledic, Ivana, Prayer, Daniela, Stuempflen, Marlene, Van Elslander, Esther, Ourselin, Sébastien, Deprest, Jan, Vercauteren, Tom
Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermin
Externí odkaz:
http://arxiv.org/abs/2204.02779
Autor:
Fidon, Lucas, Aertsen, Michael, Shit, Suprosanna, Demaerel, Philippe, Ourselin, Sébastien, Deprest, Jan, Vercauteren, Tom
This paper describes our method for our participation in the FeTA challenge2021 (team name: TRABIT). The performance of convolutional neural networks for medical image segmentation is thought to correlate positively with the number of training data.
Externí odkaz:
http://arxiv.org/abs/2111.02408
Publikováno v:
BMC Neuroscience, Vol 12, Iss Suppl 1, p P181 (2011)
Externí odkaz:
https://doaj.org/article/4f478507353b46aea838d4667834ceeb
Publikováno v:
BMC Neuroscience, Vol 12, Iss Suppl 1, p P182 (2011)
Externí odkaz:
https://doaj.org/article/ee9891a6e39b4be6a72b9eb5a7071aee
Publikováno v:
BMC Neuroscience, Vol 12, Iss Suppl 1, p P183 (2011)
Externí odkaz:
https://doaj.org/article/c2e78f4db02e4d77b1f667edba2f386c
Autor:
Fidon, Lucas, Aertsen, Michael, Mufti, Nada, Deprest, Thomas, Emam, Doaa, Guffens, Frédéric, Schwartz, Ernst, Ebner, Michael, Prayer, Daniela, Kasprian, Gregor, David, Anna L., Melbourne, Andrew, Ourselin, Sébastien, Deprest, Jan, Langs, Georg, Vercauteren, Tom
The performance of deep neural networks typically increases with the number of training images. However, not all images have the same importance towards improved performance and robustness. In fetal brain MRI, abnormalities exacerbate the variability
Externí odkaz:
http://arxiv.org/abs/2108.04175
Autor:
Kim, Tom Dongmin, Khanal, Sadhana, Bäcker, Leonard E., Lood, Cédric, Kerremans, Alison, Gorivale, Sayali, Begyn, Katrien, Cambré, Alexander, Rajkovic, Andreja, Devlieghere, Frank, Heyndrickx, Marc, Michiels, Chris, Duitama, Jorge, Aertsen, Abram
Publikováno v:
In Current Biology 22 July 2024 34(14):3077-3085
Autor:
Khanal, Sadhana, Kim, Tom Dongmin, Begyn, Katrien, Duverger, Wouter, Kramer, Gertjan, Brul, Stanley, Rajkovic, Andreja, Devlieghere, Frank, Heyndrickx, Marc, Schymkowitz, Joost, Rousseau, Frederic, Broussolle, Véronique, Michiels, Chris, Aertsen, Abram
Publikováno v:
In International Journal of Food Microbiology 16 June 2024 418
Publikováno v:
In Current Opinion in Microbiology June 2024 79