Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Warfield, Simon K."'
Autor:
Nian, Rui, Zhang, Guoyao, Sui, Yao, Qian, Yuqi, Li, Qiuying, Zhao, Mingzhang, Li, Jianhui, Gholipour, Ali, Warfield, Simon K.
Magnetic resonance imaging (MRI) is critically important for brain mapping in both scientific research and clinical studies. Precise segmentation of brain tumors facilitates clinical diagnosis, evaluations, and surgical planning. Deep learning has re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6e8d8419772ffc83395b4f02244d93da
http://arxiv.org/abs/2304.14508
http://arxiv.org/abs/2304.14508
Segmentation of brain magnetic resonance images (MRI) is crucial for the analysis of the human brain and diagnosis of various brain disorders. The drawbacks of time-consuming and error-prone manual delineation procedures are aimed to be alleviated by
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c003e69e5a9db6f154ba5c7d6ad0382
http://arxiv.org/abs/2205.09601
http://arxiv.org/abs/2205.09601
Autor:
van der Weijden, Chris WJ, van der Hoorn, Anouk, Potze, Jan Hendrik, Renken, Remco J, Borra, Ronald JH, Dierckx, Rudi AJO, Gutmann, Ingomar W, Ouaalam, Hakim, Karimi, Davood, Gholipour, Ali, Warfield, Simon K, de Vries, Erik FJ, Meilof, Jan F
Supplemental material, sj-pdf-1-jcb-10.1177_0271678X221107953 for Diffusion-derived parameters in lesions, peri-lesion, and normal-appearing white matter in multiple sclerosis using tensor, kurtosis, and fixel-based analysis by Chris WJ van der Weijd
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e4c33c35735fb8007e9a920ad8770060
It is highly desirable to know how uncertain a model's predictions are, especially for models that are complex and hard to understand as in deep learning. Although there has been a growing interest in using deep learning methods in diffusion-weighted
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cee7bd0eab27f8ac021a8f8f5aba3796
http://arxiv.org/abs/2111.10847
http://arxiv.org/abs/2111.10847
Publikováno v:
J Neuroimaging
BACKGROUND AND PURPOSE -: Functional MRI neurofeedback (fMRI-nf) leverages the brain's ability to self-regulate its own activity. However, self-regulation processes engaged during fMRI-nf are incompletely understood. Here, we used matched feedback in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::75f6d1d9fca74a44ddebc180b72733c1
https://europepmc.org/articles/PMC8440351/
https://europepmc.org/articles/PMC8440351/
Transfer learning is widely used for training machine learning models. Here, we study the role of transfer learning for training fully convolutional networks (FCNs) for medical image segmentation. Our experiments show that although transfer learning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6dfa0085bcee0b50892eeda97c1cbb49
http://arxiv.org/abs/2006.00356
http://arxiv.org/abs/2006.00356
Autor:
Ning, Lipeng, Bonet-Carne, Elisenda, Grussu, Francesco, Sepehrband, Farshid, Kaden, Enrico, Veraart, Jelle, Blumberg, Stefano B., Khoo, Can Son, Palombo, Marco, Coll-Font, Jaume, Scherrer, Benoit, Warfield, Simon K., Karayumak, Suheyla Cetin, Rathi, Yogesh, Koppers, Simon, Weninger, Leon, Ebert, Julia, Merhof, Dorit, Moyer, Daniel, Pietsch, Maximilian, Christiaens, Daan, Teixeira, Rui, Tournier, Jacques-Donald, Zhylka, Andrey, Pluim, Josien, Parker, Greg, Rudrapatna, Umesh, Evans, John, Charron, Cyril, Jones, Derek K., Tax, Chantal M.W.
Publikováno v:
27th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM2019)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=narcis______::1c462b61f07c507be1cc8211926fa299
https://research.tue.nl/nl/publications/2fb47cf7-3a63-4154-b88d-80e0d2ea108d
https://research.tue.nl/nl/publications/2fb47cf7-3a63-4154-b88d-80e0d2ea108d
Fast data acquisition in Magnetic Resonance Imaging (MRI) is vastly in demand and scan time directly depends on the number of acquired k-space samples. The data-driven methods based on deep neural networks have resulted in promising improvements, com
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::570ba04d8293b8551a8af841ad4e2662
The most recent fast and accurate image segmentation methods are built upon fully convolutional deep neural networks. In this paper, we propose new deep learning strategies for DenseNets to improve segmenting images with subtle differences in intensi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b8c010e8b6b2919ced7b93c13c0213e4
To assess the validity of the superposition approximation for crossing fascicles, i.e., the assumption that the total diffusion-weighted MRI signal is the sum of the signals arising from each fascicle independently, even when the fascicles intermingl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::545ab62b80bffad6970d735d13cb7189
https://europepmc.org/articles/PMC5770244/
https://europepmc.org/articles/PMC5770244/