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of 197
pro vyhledávání: '"Engelhardt Sandy"'
Contemporary developments in generative AI are rapidly transforming the field of medical AI. These developments have been predominantly driven by the availability of large datasets and high computing power, which have facilitated a significant increa
Externí odkaz:
http://arxiv.org/abs/2407.14892
Autor:
Tölle, Malte, Burger, Lukas, Kelm, Halvar, André, Florian, Bannas, Peter, Diller, Gerhard, Frey, Norbert, Garthe, Philipp, Groß, Stefan, Hennemuth, Anja, Kaderali, Lars, Krüger, Nina, Leha, Andreas, Martin, Simon, Meyer, Alexander, Nagel, Eike, Orwat, Stefan, Scherer, Clemens, Seiffert, Moritz, Seliger, Jan Moritz, Simm, Stefan, Friede, Tim, Seidler, Tim, Engelhardt, Sandy
Purpose: Federated training is often hindered by heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and disparities in label quality. This is particularly evident in the emerging m
Externí odkaz:
http://arxiv.org/abs/2407.09064
Autor:
Tölle, Malte, Garthe, Philipp, Scherer, Clemens, Seliger, Jan Moritz, Leha, Andreas, Krüger, Nina, Simm, Stefan, Martin, Simon, Eble, Sebastian, Kelm, Halvar, Bednorz, Moritz, André, Florian, Bannas, Peter, Diller, Gerhard, Frey, Norbert, Groß, Stefan, Hennemuth, Anja, Kaderali, Lars, Meyer, Alexander, Nagel, Eike, Orwat, Stefan, Seiffert, Moritz, Friede, Tim, Seidler, Tim, Engelhardt, Sandy
Federated learning (FL) is a renowned technique for utilizing decentralized data while preserving privacy. However, real-world applications often involve inherent challenges such as partially labeled datasets, where not all clients possess expert ann
Externí odkaz:
http://arxiv.org/abs/2407.07557
Autor:
Tölle, Malte, Navarro, Fernando, Eble, Sebastian, Wolf, Ivo, Menze, Bjoern, Engelhardt, Sandy
Federated learning is one popular paradigm to train a joint model in a distributed, privacy-preserving environment. But partial annotations pose an obstacle meaning that categories of labels are heterogeneous over clients. We propose to learn a joint
Externí odkaz:
http://arxiv.org/abs/2407.07488
Autor:
Leister, Robin, Karl, Roger, Stroh, Lubov, Mereles, Derliz, Eden, Matthias, Neff, Luis, de Simone, Raffaele, Romano, Gabriele, Kriegseis, Jochen, Karck, Matthias, Lichtenstern, Christoph, Frey, Norbert, Frohnapfel, Bettina, Stroh, Alexander, Engelhardt, Sandy
The flow convergence method includes calculation of the proximal isovelocity surface area (PISA) and is widely used to classify mitral regurgitation (MR) with echocardiography. It constitutes a primary decision factor for determination of treatment a
Externí odkaz:
http://arxiv.org/abs/2403.05224
Autor:
Dar, Salman Ul Hassan, Seyfarth, Marvin, Kahmann, Jannik, Ayx, Isabelle, Papavassiliu, Theano, Schoenberg, Stefan O., Frey, Norbert, Baeßler, Bettina, Foersch, Sebastian, Truhn, Daniel, Kather, Jakob Nikolas, Engelhardt, Sandy
AI models present a wide range of applications in the field of medicine. However, achieving optimal performance requires access to extensive healthcare data, which is often not readily available. Furthermore, the imperative to preserve patient privac
Externí odkaz:
http://arxiv.org/abs/2402.01054
Autor:
Grizelj Andela, Sharan Lalith, Karck Matthias, De Simone Raffaele, Romano Gabriele, Engelhardt Sandy
Publikováno v:
Current Directions in Biomedical Engineering, Vol 10, Iss 1, Pp 25-28 (2024)
Mitral valve regurgitation is one of the most common heart valve diseases that can occur when the structural composition of the mitral valve is affected. Mitral valve repair, typically performed in a minimally invasive setting, is a complex surgery t
Externí odkaz:
https://doaj.org/article/b9e7de7d1b5d4970ba8efd7a0fc43aab
Autor:
Dar, Salman Ul Hassan, Ghanaat, Arman, Kahmann, Jannik, Ayx, Isabelle, Papavassiliu, Theano, Schoenberg, Stefan O., Engelhardt, Sandy
Generative latent diffusion models have been established as state-of-the-art in data generation. One promising application is generation of realistic synthetic medical imaging data for open data sharing without compromising patient privacy. Despite t
Externí odkaz:
http://arxiv.org/abs/2307.01148
Autor:
Kostiuchik, Georgii, Sharan, Lalith, Mayer, Benedikt, Wolf, Ivo, Preim, Bernhard, Engelhardt, Sandy
Purpose: Machine learning models can only be reliably evaluated if training, validation, and test data splits are representative and not affected by the absence of classes of interest. Surgical workflow and instrument recognition tasks are complicate
Externí odkaz:
http://arxiv.org/abs/2306.16879