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pro vyhledávání: '"Lingg A"'
Autor:
Xu, Siying, Hammernik, Kerstin, Lingg, Andreas, Kuebler, Jens, Krumm, Patrick, Rueckert, Daniel, Gatidis, Sergios, Kuestner, Thomas
Cardiac Cine Magnetic Resonance Imaging (MRI) provides an accurate assessment of heart morphology and function in clinical practice. However, MRI requires long acquisition times, with recent deep learning-based methods showing great promise to accele
Externí odkaz:
http://arxiv.org/abs/2407.03034
Autor:
Ghoul, Aya, Pan, Jiazhen, Lingg, Andreas, Kübler, Jens, Krumm, Patrick, Hammernik, Kerstin, Rueckert, Daniel, Gatidis, Sergios, Küstner, Thomas
Accurate motion estimation at high acceleration factors enables rapid motion-compensated reconstruction in Magnetic Resonance Imaging (MRI) without compromising the diagnostic image quality. In this work, we introduce an attention-aware deep learning
Externí odkaz:
http://arxiv.org/abs/2404.17621
Autor:
Yarici, Metin C., Amadori, Pierluigi, Davies, Harry, Nakamura, Takashi, Lingg, Nico, Demiris, Yiannis, Mandic, Danilo P.
Ear EEG based driver fatigue monitoring systems have the potential to provide a seamless, efficient, and feasibly deployable alternative to existing scalp EEG based systems, which are often cumbersome and impractical. However, the feasibility of dete
Externí odkaz:
http://arxiv.org/abs/2301.06406
Autor:
Yarici, Metin, Von Rosenberg, Wilhelm, Hammour, Ghena, Davies, Harry, Amadori, Pierluigi, Lingg, Nico, Demiris, Yiannis, Mandic, Danilo P.
Wearable technologies are envisaged to provide critical support to future healthcare systems. Hearables - devices worn in the ear - are of particular interest due to their ability to provide health monitoring in an efficient, reliable and unobtrusive
Externí odkaz:
http://arxiv.org/abs/2301.02475
Human skeleton point clouds are commonly used to automatically classify and predict the behaviour of others. In this paper, we use a contrastive self-supervised learning method, SimCLR, to learn representations that capture the semantics of skeleton
Externí odkaz:
http://arxiv.org/abs/2211.05304
One of the most fundamental problems in computational learning theory is the the problem of learning a finite automaton $A$ consistent with a finite set $P$ of positive examples and with a finite set $N$ of negative examples. By consistency, we mean
Externí odkaz:
http://arxiv.org/abs/2206.10025
Autor:
Bond, Jacob, Lingg, Andrew
To evaluate the robustness of non-classifier models, we propose probabilistic local equivalence, based on the notion of randomized smoothing, as a way to quantitatively evaluate the robustness of an arbitrary function. In addition, to understand the
Externí odkaz:
http://arxiv.org/abs/2206.02539
Autor:
Judith Herrmann, You-Shan Feng, Sebastian Gassenmaier, Jan-Peter Grunz, Gregor Koerzdoerfer, Andreas Lingg, Haidara Almansour, Dominik Nickel, Ahmed E. Othman, Saif Afat
Publikováno v:
European Journal of Radiology Open, Vol 12, Iss , Pp 100557- (2024)
Purpose: The objective of this study was to implement a 5-minute MRI protocol for the shoulder in routine clinical practice consisting of accelerated 2D turbo spin echo (TSE) sequences with deep learning (DL) reconstruction at 1.5 and 3 Tesla, and to
Externí odkaz:
https://doaj.org/article/034153336cf7428989428ce8738241eb
Autor:
Ferreira-Faria, Diogo, Scheich, David, Tombak, Eva-Maria, Virumäe, Kai, Männik, Andres, Jungbauer, Alois, Lingg, Nico
Publikováno v:
In Separation and Purification Technology 6 September 2024 343
Autor:
Leite, Ana Cristina Lima, Nascimento, Thiago Pajeú, da Cunha, Márcia Nieves Carneiro, Mehari, Yirgaalem, Berger, Eva, Scheich, David, Lingg, Nico, Jungbauer, Alois
Publikováno v:
In International Journal of Biological Macromolecules August 2024 275 Part 2