Zobrazeno 1 - 10
of 548
pro vyhledávání: '"Forkert, Nils D."'
Autor:
Crespo Pimentel, Bernardo, Ingwersen, Thies, Häusler, Karl Georg, Schlemm, Eckhard, Forkert, Nils D., Rajashekar, Deepthi, Mouches, Pauline, Königsberg, Alina, Kirchhof, Paulus, Kunze, Claudia, Tütüncü, Serdar, Olma, Manuel C., Krämer, Michael, Michalski, Dominik, Kraft, Andrea, Rizos, Timolaos, Helberg, Torsten, Ehrlich, Sven, Nabavi, Darius G., Röther, Joachim, Laufs, Ulrich, Veltkamp, Roland, Heuschmann, Peter U., Cheng, Bastian, Endres, Matthias, Thomalla, Götz
Paroxysmal Atrial fibrillation (AF) is often clinically silent and may be missed by the usual diagnostic workup after ischemic stroke. We aimed to determine whether shape characteristics of ischemic stroke lesions can be used to predict AF in stroke
Externí odkaz:
https://ul.qucosa.de/id/qucosa%3A88186
https://ul.qucosa.de/api/qucosa%3A88186/attachment/ATT-0/
https://ul.qucosa.de/api/qucosa%3A88186/attachment/ATT-0/
Autor:
Stanley, Emma A. M., Souza, Raissa, Winder, Anthony, Gulve, Vedant, Amador, Kimberly, Wilms, Matthias, Forkert, Nils D.
Publikováno v:
Journal of the American Medical Informatics Association, 2024;, ocae165
Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of disparities in performance between subgroups. Since not all sources of biases in real-world medical imaging data are easily identifi
Externí odkaz:
http://arxiv.org/abs/2311.02115
Autor:
Felfeliyan, Banafshe, Hareendranathan, Abhilash, Kuntze, Gregor, Wichuk, Stephanie, Forkert, Nils D., Jaremko, Jacob L., Ronsky, Janet L.
Recent advances in deep learning algorithms have led to significant benefits for solving many medical image analysis problems. Training deep learning models commonly requires large datasets with expert-labeled annotations. However, acquiring expert-l
Externí odkaz:
http://arxiv.org/abs/2209.08172
Autor:
Felfeliyan, Banafshe, Hareendranathan, Abhilash, Kuntze, Gregor, Cornell, David, Forkert, Nils D., Jaremko, Jacob L., Ronsky, Janet L.
Many successful methods developed for medical image analysis that are based on machine learning use supervised learning approaches, which often require large datasets annotated by experts to achieve high accuracy. However, medical data annotation is
Externí odkaz:
http://arxiv.org/abs/2207.11191
This paper presents bone adaptation as a geometric flow. The proposed method is based on two assumptions: first, that the bone surface is smooth (not fractal) permitting the definition of a tangent plane and, second, that the interface between marrow
Externí odkaz:
http://arxiv.org/abs/2111.04935
An algorithm is presented for constructing high-order signed distance fields for two phase materials imaged with computed tomography. The signed distance field is high-order in that it is free of the quantization artifact associated with the distance
Externí odkaz:
http://arxiv.org/abs/2111.01350
Autor:
Gutierrez, Alejandro, Amador, Kimberly, Winder, Anthony, Wilms, Matthias, Fiehler, Jens, Forkert, Nils D.
Publikováno v:
In Computerized Medical Imaging and Graphics June 2024 114
Signed distance transforms of sampled signals can be constructed better than the traditional exact signed distance transform. Such a transform is termed the high-order signed distance transform and is defined as satisfying three conditions: the Eikon
Externí odkaz:
http://arxiv.org/abs/2110.13354
Anatomical structures such as the hippocampus, liver, and bones can be analyzed as orientable, closed surfaces. This permits the computation of volume, surface area, mean curvature, Gaussian curvature, and the Euler-Poincar\'e characteristic as well
Externí odkaz:
http://arxiv.org/abs/2108.04354
Autor:
Aponte, J. David, Bannister, Jordan J., Hoskens, Hanne, Matthews, Harold, Katsura, Kaitlin, Da Silva, Cassidy, Cruz, Tim, Pilz, Julie H.M., Spritz, Richard A., Forkert, Nils D., Claes, Peter, Bernier, Francois P., Klein, Ophir D., Katz, David C., Hallgrímsson, Benedikt
Publikováno v:
In The American Journal of Human Genetics 4 January 2024 111(1):39-47