Výsledky vyhledávání - "Isfort Peter"

Akademický článek

How to Support Students in Maximizing their Outcome with Online Cooperation?

Autor: Benninghoff Mark J., Isfort Peter, Baumann Martin

Publikováno v: Current Directions in Biomedical Engineering, Vol 7, Iss 2, Pp 260-263 (2021)

During the last year, student cooperation has shifted to the digital space. Chats, videoconferencing and online collaboration tools replace physical meetings and face-to-face interactions. While many online tools provide sophisticated technologies to

Externí odkaz: https://doaj.org/article/6b04e352820843e3859b55e6987f1e5c

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Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI Models

Autor: Arasteh, Soroosh Tayebi, Kuhl, Christiane, Saehn, Marwin-Jonathan, Isfort, Peter, Truhn, Daniel, Nebelung, Sven

Publikováno v: Sci Rep 13, 22576 (2023)

Developing robust artificial intelligence (AI) models that generalize well to unseen datasets is challenging and usually requires large and variable datasets, preferably from multiple institutions. In federated learning (FL), a model is trained colla

Externí odkaz: http://arxiv.org/abs/2310.00757

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Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy

Autor: Arasteh, Soroosh Tayebi, Lotfinia, Mahshad, Nolte, Teresa, Saehn, Marwin, Isfort, Peter, Kuhl, Christiane, Nebelung, Sven, Kaissis, Georgios, Truhn, Daniel

Publikováno v: Radiology: Artificial Intelligence, 2024, 6(1), e230212

Developing robust and effective artificial intelligence (AI) models in medicine requires access to large amounts of patient data. The use of AI models solely trained on large multi-institutional datasets can help with this, yet the imperative to ensu

Externí odkaz: http://arxiv.org/abs/2306.06503

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Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels

Autor: Arasteh, Soroosh Tayebi, Isfort, Peter, Saehn, Marwin, Mueller-Franzes, Gustav, Khader, Firas, Kather, Jakob Nikolas, Kuhl, Christiane, Nebelung, Sven, Truhn, Daniel

Publikováno v: Sci Rep 13, 6046 (2023)

Due to the rapid advancements in recent years, medical image analysis is largely dominated by deep learning (DL). However, building powerful and robust DL models requires training with large multi-party datasets. While multiple stakeholders have prov

Externí odkaz: http://arxiv.org/abs/2211.13606

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Image prediction of disease progression by style-based manifold extrapolation

Autor: Han, Tianyu, Kather, Jakob Nikolas, Pedersoli, Federico, Zimmermann, Markus, Keil, Sebastian, Schulze-Hagen, Maximilian, Terwoelbeck, Marc, Isfort, Peter, Haarburger, Christoph, Kiessling, Fabian, Schulz, Volkmar, Kuhl, Christiane, Nebelung, Sven, Truhn, Daniel

Disease-modifying management aims to prevent deterioration and progression of the disease, not just relieve symptoms. Unfortunately, the development of necessary therapies is often hampered by the failure to recognize the presymptomatic disease and l

Externí odkaz: http://arxiv.org/abs/2111.11439

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