Zobrazeno 1 - 10
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pro vyhledávání: '"Schinz, A."'
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:
Sinhamahapatra, Poulami, Shit, Suprosanna, Sekuboyina, Anjany, Husseini, Malek, Schinz, David, Lenhart, Nicolas, Menze, Joern, Kirschke, Jan, Roscher, Karsten, Guennemann, Stephan
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2024)
Vertebral fracture grading classifies the severity of vertebral fractures, which is a challenging task in medical imaging and has recently attracted Deep Learning (DL) models. Only a few works attempted to make such models human-interpretable despite
Externí odkaz:
http://arxiv.org/abs/2404.02830
Autor:
Keicher, Matthias, Atad, Matan, Schinz, David, Gersing, Alexandra S., Foreman, Sarah C., Goller, Sophia S., Weissinger, Juergen, Rischewski, Jon, Dietrich, Anna-Sophia, Wiestler, Benedikt, Kirschke, Jan S., Navab, Nassir
Vertebral fractures are a consequence of osteoporosis, with significant health implications for affected patients. Unfortunately, grading their severity using CT exams is hard and subjective, motivating automated grading methods. However, current app
Externí odkaz:
http://arxiv.org/abs/2303.12031
Autor:
Engstler, Paul, Keicher, Matthias, Schinz, David, Mach, Kristina, Gersing, Alexandra S., Foreman, Sarah C., Goller, Sophia S., Weissinger, Juergen, Rischewski, Jon, Dietrich, Anna-Sophia, Wiestler, Benedikt, Kirschke, Jan S., Khakzar, Ashkan, Navab, Nassir
Do black-box neural network models learn clinically relevant features for fracture diagnosis? The answer not only establishes reliability quenches scientific curiosity but also leads to explainable and verbose findings that can assist the radiologist
Externí odkaz:
http://arxiv.org/abs/2203.16273
Autor:
Liebl, Hans, Schinz, David, Sekuboyina, Anjany, Malagutti, Luca, Löffler, Maximilian T., Bayat, Amirhossein, Husseini, Malek El, Tetteh, Giles, Grau, Katharina, Niederreiter, Eva, Baum, Thomas, Wiestler, Benedikt, Menze, Bjoern, Braren, Rickmer, Zimmer, Claus, Kirschke, Jan S.
With the advent of deep learning algorithms, fully automated radiological image analysis is within reach. In spine imaging, several atlas- and shape-based as well as deep learning segmentation algorithms have been proposed, allowing for subsequent au
Externí odkaz:
http://arxiv.org/abs/2103.06360
Autor:
Wiltgen, Tun, McGinnis, Julian, Schlaeger, Sarah, Kofler, Florian, Voon, CuiCi, Berthele, Achim, Bischl, Daria, Grundl, Lioba, Will, Nikolaus, Metz, Marie, Schinz, David, Sepp, Dominik, Prucker, Philipp, Schmitz-Koep, Benita, Zimmer, Claus, Menze, Bjoern, Rueckert, Daniel, Hemmer, Bernhard, Kirschke, Jan, Mühlau, Mark, Wiestler, Benedikt
Publikováno v:
In NeuroImage: Clinical 2024 42
Autor:
Tun Wiltgen, Julian McGinnis, Sarah Schlaeger, Florian Kofler, CuiCi Voon, Achim Berthele, Daria Bischl, Lioba Grundl, Nikolaus Will, Marie Metz, David Schinz, Dominik Sepp, Philipp Prucker, Benita Schmitz-Koep, Claus Zimmer, Bjoern Menze, Daniel Rueckert, Bernhard Hemmer, Jan Kirschke, Mark Mühlau, Benedikt Wiestler
Publikováno v:
NeuroImage: Clinical, Vol 42, Iss , Pp 103611- (2024)
Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an engineered lesion segmentation tool, LST. While recent lesion segment
Externí odkaz:
https://doaj.org/article/282946db78e7480b84215979b2e318c5
Akademický článek
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Autor:
David Schinz, Benita Schmitz-Koep, Marlene Tahedl, Timo Teckenberg, Vivian Schultz, Julia Schulz, Claus Zimmer, Christian Sorg, Christian Gaser, Dennis M. Hedderich
Publikováno v:
Frontiers in Psychiatry, Vol 14 (2023)
BackgroundCocaine use disorder (CUD) is a global health issue with severe behavioral and cognitive sequelae. While previous evidence suggests a variety of structural and age-related brain changes in CUD, the impact on both, cortical thickness and bra
Externí odkaz:
https://doaj.org/article/da779308a2d643549a3e380d5abfd17b
Autor:
Schmitz-Koep, Benita, Menegaux, Aurore, Gaser, Christian, Brandes, Elin, Schinz, David, Thalhammer, Melissa, Daamen, Marcel, Boecker, Henning, Zimmer, Claus, Priller, Josef, Wolke, Dieter, Bartmann, Peter, Sorg, Christian, Hedderich, Dennis M.
Publikováno v:
In Biological Psychiatry: Cognitive Neuroscience and Neuroimaging May 2023 8(5):495-504