Zobrazeno 1 - 10
of 15 512
pro vyhledávání: '"Johannes, C"'
Autor:
Berger, Alexander H., Lux, Laurin, Weers, Alexander, Menten, Martin, Rueckert, Daniel, Paetzold, Johannes C.
Topological correctness, i.e., the preservation of structural integrity and specific characteristics of shape, is a fundamental requirement for medical imaging tasks, such as neuron or vessel segmentation. Despite the recent surge in topology-aware m
Externí odkaz:
http://arxiv.org/abs/2412.14619
Quantum dots (QDs) are pivotal for the development of quantum technologies, with applications ranging from single-photon sources for secure communication to quantum computing infrastructures. Understanding the electron dynamics within these QDs is es
Externí odkaz:
http://arxiv.org/abs/2412.14893
Autor:
Ganesan, Adithya V, Varadarajan, Vasudha, Lal, Yash Kumar, Eijsbroek, Veerle C., Kjell, Katarina, Kjell, Oscar N. E., Dhanasekaran, Tanuja, Stade, Elizabeth C., Eichstaedt, Johannes C., Boyd, Ryan L., Schwartz, H. Andrew, Flek, Lucie
Use of large language models such as ChatGPT (GPT-4) for mental health support has grown rapidly, emerging as a promising route to assess and help people with mood disorders, like depression. However, we have a limited understanding of GPT-4's schema
Externí odkaz:
http://arxiv.org/abs/2411.13800
Autor:
Lux, Laurin, Berger, Alexander H., Weers, Alexander, Stucki, Nico, Rueckert, Daniel, Bauer, Ulrich, Paetzold, Johannes C.
Topological correctness plays a critical role in many image segmentation tasks, yet most networks are trained using pixel-wise loss functions, such as Dice, neglecting topological accuracy. Existing topology-aware methods often lack robust topologica
Externí odkaz:
http://arxiv.org/abs/2411.03228
Autor:
Mächler, Leon, Grimberg, Gustav, Ezhov, Ivan, Nickel, Manuel, Shit, Suprosanna, Naccache, David, Paetzold, Johannes C.
This paper presents FedPID, our submission to the Federated Tumor Segmentation Challenge 2024 (FETS24). Inspired by FedCostWAvg and FedPIDAvg, our winning contributions to FETS21 and FETS2022, we propose an improved aggregation strategy for federated
Externí odkaz:
http://arxiv.org/abs/2411.02152
Autor:
Klimek, Anton, Heyn, Johannes C. J., Mondal, Debasmita, Schwartz, Sophia, Rädler, Joachim O., Sharma, Prerna, Block, Stephan, Netz, Roland R.
When analyzing the individual positional dynamics of an ensemble of moving objects, the extracted parameters that characterize the motion of individual objects, such as the mean-squared instantaneous velocity or the diffusivity, exhibit a spread that
Externí odkaz:
http://arxiv.org/abs/2410.14561
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:
Balcerak, Michal, Amiranashvili, Tamaz, Wagner, Andreas, Weidner, Jonas, Karnakov, Petr, Paetzold, Johannes C., Ezhov, Ivan, Koumoutsakos, Petros, Wiestler, Benedikt, Menze, Bjoern
Physical models in the form of partial differential equations serve as important priors for many under-constrained problems. One such application is tumor treatment planning, which relies on accurately estimating the spatial distribution of tumor cel
Externí odkaz:
http://arxiv.org/abs/2409.20409
Autor:
Prabhakar, Chinmay, Shit, Suprosanna, Musio, Fabio, Yang, Kaiyuan, Amiranashvili, Tamaz, Paetzold, Johannes C., Li, Hongwei Bran, Menze, Bjoern
Blood vessel networks, represented as 3D graphs, help predict disease biomarkers, simulate blood flow, and aid in synthetic image generation, relevant in both clinical and pre-clinical settings. However, generating realistic vessel graphs that corres
Externí odkaz:
http://arxiv.org/abs/2407.05842
In this work, we propose an efficient algorithm for the calculation of the Betti matching, which can be used as a loss function to train topology aware segmentation networks. Betti matching loss builds on techniques from topological data analysis, sp
Externí odkaz:
http://arxiv.org/abs/2407.04683