Zobrazeno 1 - 10
of 85
pro vyhledávání: '"Christiane Kuhl"'
Autor:
Tianyu Han, Sven Nebelung, Firas Khader, Tianci Wang, Gustav Müller-Franzes, Christiane Kuhl, Sebastian Försch, Jens Kleesiek, Christoph Haarburger, Keno K. Bressem, Jakob Nikolas Kather, Daniel Truhn
Publikováno v:
npj Digital Medicine, Vol 7, Iss 1, Pp 1-9 (2024)
Abstract Large language models (LLMs) have broad medical knowledge and can reason about medical information across many domains, holding promising potential for diverse medical applications in the near future. In this study, we demonstrate a concerni
Externí odkaz:
https://doaj.org/article/e0f82984e9bc430d804e6ddc1ed52b3c
Autor:
Teresa Lemainque, Nicola Pridöhl, Shuo Zhang, Marc Huppertz, Manuel Post, Can Yüksel, Masami Yoneyama, Andreas Prescher, Christiane Kuhl, Daniel Truhn, Sven Nebelung
Publikováno v:
European Radiology Experimental, Vol 8, Iss 1, Pp 1-12 (2024)
Abstract Background Quantitative techniques such as T2 and T1ρ mapping allow evaluating the cartilage and meniscus. We evaluated multi-interleaved X-prepared turbo-spin echo with intuitive relaxometry (MIXTURE) sequences with turbo spin-echo (TSE) c
Externí odkaz:
https://doaj.org/article/59f5edc89d244d629337e31d02191578
Autor:
Gustav Müller-Franzes, Luisa Huck, Maike Bode, Sven Nebelung, Christiane Kuhl, Daniel Truhn, Teresa Lemainque
Publikováno v:
European Radiology Experimental, Vol 8, Iss 1, Pp 1-13 (2024)
Abstract Background To compare denoising diffusion probabilistic models (DDPM) and generative adversarial networks (GAN) for recovering contrast-enhanced breast magnetic resonance imaging (MRI) subtraction images from virtual low-dose subtraction ima
Externí odkaz:
https://doaj.org/article/0a9f02dfb94e457f85041a2a31b2dede
Autor:
Tianyu Han, Laura Žigutytė, Luisa Huck, Marc Sebastian Huppertz, Robert Siepmann, Yossi Gandelsman, Christian Blüthgen, Firas Khader, Christiane Kuhl, Sven Nebelung, Jakob Nikolas Kather, Daniel Truhn
Publikováno v:
Cell Reports Medicine, Vol 5, Iss 9, Pp 101713- (2024)
Summary: Reliably detecting potentially misleading patterns in automated diagnostic assistance systems, such as those powered by artificial intelligence (AI), is crucial for instilling user trust and ensuring reliability. Current techniques fall shor
Externí odkaz:
https://doaj.org/article/30d6b4c960ef486e997201c823bc662b
Autor:
Soroosh Tayebi Arasteh, Alexander Ziller, Christiane Kuhl, Marcus Makowski, Sven Nebelung, Rickmer Braren, Daniel Rueckert, Daniel Truhn, Georgios Kaissis
Publikováno v:
Communications Medicine, Vol 4, Iss 1, Pp 1-12 (2024)
Abstract Background Artificial intelligence (AI) models are increasingly used in the medical domain. However, as medical data is highly sensitive, special precautions to ensure its protection are required. The gold standard for privacy preservation i
Externí odkaz:
https://doaj.org/article/5debbebf04fd4e9d94555bf1fa6d9f67
Autor:
Soroosh Tayebi Arasteh, Tianyu Han, Mahshad Lotfinia, Christiane Kuhl, Jakob Nikolas Kather, Daniel Truhn, Sven Nebelung
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract A knowledge gap persists between machine learning (ML) developers (e.g., data scientists) and practitioners (e.g., clinicians), hampering the full utilization of ML for clinical data analysis. We investigated the potential of the ChatGPT Adv
Externí odkaz:
https://doaj.org/article/9d25270b5d584babaeb527b598dba2b5
Autor:
Soroosh Tayebi Arasteh, Christiane Kuhl, Marwin-Jonathan Saehn, Peter Isfort, Daniel Truhn, Sven Nebelung
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Abstract 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 trai
Externí odkaz:
https://doaj.org/article/f36d788857b04c089eade8cdc781ab7a
Autor:
Daniel Truhn, Christian D. Weber, Benedikt J. Braun, Keno Bressem, Jakob N. Kather, Christiane Kuhl, Sven Nebelung
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-9 (2023)
Abstract Large language models (LLMs) have shown potential in various applications, including clinical practice. However, their accuracy and utility in providing treatment recommendations for orthopedic conditions remain to be investigated. Thus, thi
Externí odkaz:
https://doaj.org/article/a36d3ce780e443e59c305d5c6f3b25a3
Autor:
Gustav Müller-Franzes, Fritz Müller-Franzes, Luisa Huck, Vanessa Raaff, Eva Kemmer, Firas Khader, Soroosh Tayebi Arasteh, Teresa Lemainque, Jakob Nikolas Kather, Sven Nebelung, Christiane Kuhl, Daniel Truhn
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-9 (2023)
Abstract Accurate and automatic segmentation of fibroglandular tissue in breast MRI screening is essential for the quantification of breast density and background parenchymal enhancement. In this retrospective study, we developed and evaluated a tran
Externí odkaz:
https://doaj.org/article/93654e36e3f44de6aa21540973bfc6bc
Autor:
Gustav Müller-Franzes, Jan Moritz Niehues, Firas Khader, Soroosh Tayebi Arasteh, Christoph Haarburger, Christiane Kuhl, Tianci Wang, Tianyu Han, Teresa Nolte, Sven Nebelung, Jakob Nikolas Kather, Daniel Truhn
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-10 (2023)
Abstract Although generative adversarial networks (GANs) can produce large datasets, their limited diversity and fidelity have been recently addressed by denoising diffusion probabilistic models, which have demonstrated superiority in natural image s
Externí odkaz:
https://doaj.org/article/d86a20b08d6d4d73b3035c5f2ff0fb9f