Zobrazeno 1 - 10
of 12 366
pro vyhledávání: '"Jan, S. A."'
Large language models have gained widespread popularity for their ability to process natural language inputs and generate insights derived from their training data, nearing the qualities of true artificial intelligence. This advancement has prompted
Externí odkaz:
http://arxiv.org/abs/2411.14513
Autor:
Graf, Robert, Hunecke, Florian, Pohl, Soeren, Atad, Matan, Moeller, Hendrik, Starck, Sophie, Kroencke, Thomas, Bette, Stefanie, Bamberg, Fabian, Pischon, Tobias, Niendorf, Thoralf, Schmidt, Carsten, Paetzold, Johannes C., Rueckert, Daniel, Kirschke, Jan S
Deep learning has made significant strides in medical imaging, leveraging the use of large datasets to improve diagnostics and prognostics. However, large datasets often come with inherent errors through subject selection and acquisition. In this pap
Externí odkaz:
http://arxiv.org/abs/2410.10220
Autor:
Riedel, Evamaria O., de la Rosa, Ezequiel, Baran, The Anh, Petzsche, Moritz Hernandez, Baazaoui, Hakim, Yang, Kaiyuan, Robben, David, Seia, Joaquin Oscar, Wiest, Roland, Reyes, Mauricio, Su, Ruisheng, Zimmer, Claus, Boeckh-Behrens, Tobias, Berndt, Maria, Menze, Bjoern, Wiestler, Benedikt, Wegener, Susanne, Kirschke, Jan S.
Stroke remains a leading cause of global morbidity and mortality, placing a heavy socioeconomic burden. Over the past decade, advances in endovascular reperfusion therapy and the use of CT and MRI imaging for treatment guidance have significantly imp
Externí odkaz:
http://arxiv.org/abs/2408.11142
Autor:
de la Rosa, Ezequiel, Su, Ruisheng, Reyes, Mauricio, Wiest, Roland, Riedel, Evamaria O., Kofler, Florian, Yang, Kaiyuan, Baazaoui, Hakim, Robben, David, Wegener, Susanne, Kirschke, Jan S., Wiestler, Benedikt, Menze, Bjoern
Accurate estimation of core (irreversibly damaged tissue) and penumbra (salvageable tissue) volumes is essential for ischemic stroke treatment decisions. Perfusion CT, the clinical standard, estimates these volumes but is affected by variations in de
Externí odkaz:
http://arxiv.org/abs/2408.10966
Autor:
Caldana, Matteo, Hesthaven, Jan S.
Neural Ordinary Differential Equations (ODEs) represent a significant advancement at the intersection of machine learning and dynamical systems, offering a continuous-time analog to discrete neural networks. Despite their promise, deploying neural OD
Externí odkaz:
http://arxiv.org/abs/2408.06073
Autor:
Atad, Matan, Gruber, Gabriel, Ribeiro, Marx, Nicolini, Luis Fernando, Graf, Robert, Möller, Hendrik, Nispel, Kati, Ezhov, Ivan, Rueckert, Daniel, Kirschke, Jan S.
Accurate calibration of finite element (FE) models is essential across various biomechanical applications, including human intervertebral discs (IVDs), to ensure their reliability and use in diagnosing and planning treatments. However, traditional ca
Externí odkaz:
http://arxiv.org/abs/2408.06067
Autor:
Atad, Matan, Schinz, David, Moeller, Hendrik, Graf, Robert, Wiestler, Benedikt, Rueckert, Daniel, Navab, Nassir, Kirschke, Jan S., Keicher, Matthias
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2024)
Counterfactual explanations (CEs) aim to enhance the interpretability of machine learning models by illustrating how alterations in input features would affect the resulting predictions. Common CE approaches require an additional model and are typica
Externí odkaz:
http://arxiv.org/abs/2408.01571
Autor:
Kratochvila, Lukas, de Jong, Gijs, Arkesteijn, Monique, Bilik, Simon, Zemcik, Tomas, Horak, Karel, Rellermeyer, Jan S.
Digital twins have a major potential to form a significant part of urban management in emergency planning, as they allow more efficient designing of the escape routes, better orientation in exceptional situations, and faster rescue intervention. Neve
Externí odkaz:
http://arxiv.org/abs/2408.01526
We investigate reduced-order models for acoustic and electromagnetic wave problems in parametrically defined domains. The parameter-to-solution maps are approximated following the so-called Galerkin POD-NN method, which combines the construction of a
Externí odkaz:
http://arxiv.org/abs/2406.13567
This work presents a novel resolution-invariant model order reduction strategy for multifidelity applications. We base our architecture on a novel neural network layer developed in this work, the graph feedforward network, which extends the concept o
Externí odkaz:
http://arxiv.org/abs/2406.03569