Zobrazeno 1 - 10
of 639
pro vyhledávání: '"Penzkofer, P."'
Autor:
Bisson, Tom, O, Isil Dogan, Piwonski, Iris, Kiehl, Tim-Rasmus, Baumgärtner, Georg Lukas, Carvalho, Rita, Hufnagl, Peter, Penzkofer, Tobias, Zerbe, Norman, Elezkurtaj, Sefer
Surgical treatment for prostate cancer often involves organ removal, i.e., prostatectomy. Pathology reports on these specimens convey treatment-relevant information. Beyond these reports, the diagnostic process generates extensive and complex informa
Externí odkaz:
http://arxiv.org/abs/2412.01855
Autor:
Müller, Philipp, Balazia, Michal, Baur, Tobias, Dietz, Michael, Heimerl, Alexander, Penzkofer, Anna, Schiller, Dominik, Brémond, François, Alexandersson, Jan, André, Elisabeth, Bulling, Andreas
Estimating the momentary level of participant's engagement is an important prerequisite for assistive systems that support human interactions. Previous work has addressed this task in within-domain evaluation scenarios, i.e. training and testing on t
Externí odkaz:
http://arxiv.org/abs/2408.16625
Autor:
Li, Hao, Liu, Han, von Busch, Heinrich, Grimm, Robert, Huisman, Henkjan, Tong, Angela, Winkel, David, Penzkofer, Tobias, Shabunin, Ivan, Choi, Moon Hyung, Yang, Qingsong, Szolar, Dieter, Shea, Steven, Coakley, Fergus, Harisinghani, Mukesh, Oguz, Ipek, Comaniciu, Dorin, Kamen, Ali, Lou, Bin
Publikováno v:
Radiology: Artificial Intelligence 2024;6(5):e230521
Our hypothesis is that UDA using diffusion-weighted images, generated with a unified model, offers a promising and reliable strategy for enhancing the performance of supervised learning models in multi-site prostate lesion detection, especially when
Externí odkaz:
http://arxiv.org/abs/2408.04777
We introduce the Overcooked Generalisation Challenge (OGC) - the first benchmark to study agents' zero-shot cooperation abilities when faced with novel partners and levels in the Overcooked-AI environment. This perspective starkly contrasts a large b
Externí odkaz:
http://arxiv.org/abs/2406.17949
Autor:
Bujotzek, Markus R., Akünal, Ünal, Denner, Stefan, Neher, Peter, Zenk, Maximilian, Frodl, Eric, Jaiswal, Astha, Kim, Moon, Krekiehn, Nicolai R., Nickel, Manuel, Ruppel, Richard, Both, Marcus, Döllinger, Felix, Opitz, Marcel, Persigehl, Thorsten, Kleesiek, Jens, Penzkofer, Tobias, Maier-Hein, Klaus, Braren, Rickmer, Bucher, Andreas
Objective: Federated Learning (FL) enables collaborative model training while keeping data locally. Currently, most FL studies in radiology are conducted in simulated environments due to numerous hurdles impeding its translation into practice. The fe
Externí odkaz:
http://arxiv.org/abs/2405.09409
While Vector Symbolic Architectures (VSAs) are promising for modelling spatial cognition, their application is currently limited to artificially generated images and simple spatial queries. We propose VSA4VQA - a novel 4D implementation of VSAs that
Externí odkaz:
http://arxiv.org/abs/2405.03852
Autor:
Denner, Stefan, Zimmerer, David, Bounias, Dimitrios, Bujotzek, Markus, Xiao, Shuhan, Kausch, Lisa, Schader, Philipp, Penzkofer, Tobias, Jäger, Paul F., Maier-Hein, Klaus
Content-based image retrieval (CBIR) has the potential to significantly improve diagnostic aid and medical research in radiology. Current CBIR systems face limitations due to their specialization to certain pathologies, limiting their utility. In res
Externí odkaz:
http://arxiv.org/abs/2403.06567
Autor:
Denner, Stefan, Scherer, Jonas, Kades, Klaus, Bounias, Dimitrios, Schader, Philipp, Kausch, Lisa, Bujotzek, Markus, Bucher, Andreas Michael, Penzkofer, Tobias, Maier-Hein, Klaus
In the rapidly evolving field of medical imaging, machine learning algorithms have become indispensable for enhancing diagnostic accuracy. However, the effectiveness of these algorithms is contingent upon the availability and organization of high-qua
Externí odkaz:
http://arxiv.org/abs/2309.17285
Autor:
Penzkofer, Anna, Schaefer, Simon, Strohm, Florian, Bâce, Mihai, Leutenegger, Stefan, Bulling, Andreas
While deep reinforcement learning (RL) agents outperform humans on an increasing number of tasks, training them requires data equivalent to decades of human gameplay. Recent hierarchical RL methods have increased sample efficiency by incorporating in
Externí odkaz:
http://arxiv.org/abs/2306.11483
DAG-based DLTs allow for parallel, asynchronous writing access to a ledger. Consequently, the perception of the most recent blocks may differ considerably between nodes, and the underlying network properties of the P2P layer have a direct impact on t
Externí odkaz:
http://arxiv.org/abs/2305.01232