Zobrazeno 1 - 10
of 709
pro vyhledávání: '"P. Aertsen"'
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
Autor:
Daniel Berdejo, Julien Mortier, Alexander Cambré, Malgorzata Sobota, Ronald Van Eyken, Tom Dongmin Kim, Kristof Vanoirbeek, Diego García Gonzalo, Rafael Pagán, Médéric Diard, Abram Aertsen
Publikováno v:
mBio, Vol 15, Iss 3 (2024)
ABSTRACT Understanding the evolutionary dynamics of foodborne pathogens throughout our food production chain is of utmost importance. In this study, we reveal that Salmonella Typhimurium can readily and reproducibly acquire vastly increased heat shoc
Externí odkaz:
https://doaj.org/article/6dc8d48a63b0436abf8c60e01d35f604
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:
Fidon, Lucas, Aertsen, Michael, Emam, Doaa, Mufti, Nada, Guffens, Frédéric, Deprest, Thomas, Demaerel, Philippe, David, Anna L., Melbourne, Andrew, Ourselin, Sébastien, Deprest, Jan, Vercauteren, Tom
Deep neural networks have increased the accuracy of automatic segmentation, however, their accuracy depends on the availability of a large number of fully segmented images. Methods to train deep neural networks using images for which some, but not al
Externí odkaz:
http://arxiv.org/abs/2107.03846
Autor:
Luo, Xiangde, Wang, Guotai, Song, Tao, Zhang, Jingyang, Aertsen, Michael, Deprest, Jan, Ourselin, Sebastien, Vercauteren, Tom, Zhang, Shaoting
Segmentation of organs or lesions from medical images plays an essential role in many clinical applications such as diagnosis and treatment planning. Though Convolutional Neural Networks (CNN) have achieved the state-of-the-art performance for automa
Externí odkaz:
http://arxiv.org/abs/2104.12166
Autor:
Gu, Ran, Wang, Guotai, Song, Tao, Huang, Rui, Aertsen, Michael, Deprest, Jan, Ourselin, Sébastien, Vercauteren, Tom, Zhang, Shaoting
Accurate medical image segmentation is essential for diagnosis and treatment planning of diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they are still chall
Externí odkaz:
http://arxiv.org/abs/2009.10549
Autor:
Wang, Guotai, Aertsen, Michael, Deprest, Jan, Ourselin, Sebastien, Vercauteren, Tom, Zhang, Shaoting
Segmentation of the fetal brain from stacks of motion-corrupted fetal MRI slices is important for motion correction and high-resolution volume reconstruction. Although Convolutional Neural Networks (CNNs) have been widely used for automatic segmentat
Externí odkaz:
http://arxiv.org/abs/2007.00833
Autor:
Fidon, Lucas, Aertsen, Michael, Deprest, Thomas, Emam, Doaa, Guffens, Frédéric, Mufti, Nada, Van Elslander, Esther, Schwartz, Ernst, Ebner, Michael, Prayer, Daniela, Kasprian, Gregor, David, Anna L., Melbourne, Andrew, Ourselin, Sébastien, Deprest, Jan, Langs, Georg, Vercauteren, Tom
Limiting failures of machine learning systems is of paramount importance for safety-critical applications. In order to improve the robustness of machine learning systems, Distributionally Robust Optimization (DRO) has been proposed as a generalizatio
Externí odkaz:
http://arxiv.org/abs/2001.02658