Zobrazeno 1 - 10
of 207
pro vyhledávání: '"Bressem, Keno K"'
Autor:
Dorfner, Felix J., Dada, Amin, Busch, Felix, Makowski, Marcus R., Han, Tianyu, Truhn, Daniel, Kleesiek, Jens, Sushil, Madhumita, Lammert, Jacqueline, Adams, Lisa C., Bressem, Keno K.
Large language models (LLMs) have shown potential in biomedical applications, leading to efforts to fine-tune them on domain-specific data. However, the effectiveness of this approach remains unclear. This study evaluates the performance of biomedica
Externí odkaz:
http://arxiv.org/abs/2408.13833
Autor:
Dorfner, Felix J., Vahldiek, Janis L., Donle, Leonhard, Zhukov, Andrei, Xu, Lina, Häntze, Hartmut, Makowski, Marcus R., Aerts, Hugo J. W. L., Proft, Fabian, Rodriguez, Valeria Rios, Rademacher, Judith, Protopopov, Mikhail, Haibel, Hildrun, Diekhoff, Torsten, Torgutalp, Murat, Adams, Lisa C., Poddubnyy, Denis, Bressem, Keno K.
Purpose: To examine whether incorporating anatomical awareness into a deep learning model can improve generalizability and enable prediction of disease progression. Methods: This retrospective multicenter study included conventional pelvic radiograph
Externí odkaz:
http://arxiv.org/abs/2405.07369
Autor:
Häntze, Hartmut, Xu, Lina, Mertens, Christian J., Dorfner, Felix J., Donle, Leonhard, Busch, Felix, Kader, Avan, Ziegelmayer, Sebastian, Bayerl, Nadine, Navab, Nassir, Rueckert, Daniel, Schnabel, Julia, Aerts, Hugo JWL, Truhn, Daniel, Bamberg, Fabian, Weiß, Jakob, Schlett, Christopher L., Ringhof, Steffen, Niendorf, Thoralf, Pischon, Tobias, Kauczor, Hans-Ulrich, Nonnenmacher, Tobias, Kröncke, Thomas, Völzke, Henry, Schulz-Menger, Jeanette, Maier-Hein, Klaus, Prokop, Mathias, van Ginneken, Bram, Hering, Alessa, Makowski, Marcus R., Adams, Lisa C., Bressem, Keno K.
Purpose: To develop and evaluate a deep learning model for multi-organ segmentation of MRI scans. Materials and Methods: The model was trained on 1,200 manually annotated 3D axial MRI scans from the UK Biobank, 221 in-house MRI scans, and 1228 CT sca
Externí odkaz:
http://arxiv.org/abs/2405.06463
Autor:
Häntze, Hartmut, Xu, Lina, Rattunde, Maximilian, Donle, Leonhard, Dorfner, Felix J., Hering, Alessa, Adams, Lisa C., Bressem, Keno K.
Computed tomography (CT) segmentation models often contain classes that are not currently supported by magnetic resonance imaging (MRI) segmentation models. In this study, we show that a simple image inversion technique can significantly improve the
Externí odkaz:
http://arxiv.org/abs/2405.03713
Autor:
Dorfner, Felix J., Jürgensen, Liv, Donle, Leonhard, Mohamad, Fares Al, Bodenmann, Tobias R., Cleveland, Mason C., Busch, Felix, Adams, Lisa C., Sato, James, Schultz, Thomas, Kim, Albert E., Merkow, Jameson, Bressem, Keno K., Bridge, Christopher P.
Introduction: With the rapid advances in large language models (LLMs), there have been numerous new open source as well as commercial models. While recent publications have explored GPT-4 in its application to extracting information of interest from
Externí odkaz:
http://arxiv.org/abs/2402.12298
Autor:
Han, Tianyu, Nebelung, Sven, Khader, Firas, Wang, Tianci, Mueller-Franzes, Gustav, Kuhl, Christiane, Försch, Sebastian, Kleesiek, Jens, Haarburger, Christoph, Bressem, Keno K., Kather, Jakob Nikolas, Truhn, Daniel
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 concerning vulner
Externí odkaz:
http://arxiv.org/abs/2309.17007
Autor:
Han, Tianyu, Adams, Lisa C., Papaioannou, Jens-Michalis, Grundmann, Paul, Oberhauser, Tom, Löser, Alexander, Truhn, Daniel, Bressem, Keno K.
As large language models (LLMs) like OpenAI's GPT series continue to make strides, we witness the emergence of artificial intelligence applications in an ever-expanding range of fields. In medicine, these LLMs hold considerable promise for improving
Externí odkaz:
http://arxiv.org/abs/2304.08247
Autor:
Bressem, Keno K., Papaioannou, Jens-Michalis, Grundmann, Paul, Borchert, Florian, Adams, Lisa C., Liu, Leonhard, Busch, Felix, Xu, Lina, Loyen, Jan P., Niehues, Stefan M., Augustin, Moritz, Grosser, Lennart, Makowski, Marcus R., Aerts, Hugo JWL., Löser, Alexander
Publikováno v:
Expert Systems with Applications 2024;237(21):121598
This paper presents medBERTde, a pre-trained German BERT model specifically designed for the German medical domain. The model has been trained on a large corpus of 4.7 Million German medical documents and has been shown to achieve new state-of-the-ar
Externí odkaz:
http://arxiv.org/abs/2303.08179
Autor:
Adams, Lisa C., Busch, Felix, Truhn, Daniel, Makowski, Marcus R., Aerts, Hugo JWL., Bressem, Keno K.
Publikováno v:
J Med Internet Res 2023;25:e43110
Generative models such as DALL-E 2 could represent a promising future tool for image generation, augmentation, and manipulation for artificial intelligence research in radiology provided that these models have sufficient medical domain knowledge. Her
Externí odkaz:
http://arxiv.org/abs/2209.13696
Autor:
Busch, Felix, Prucker, Philipp, Komenda, Alexander, Ziegelmayer, Sebastian, Makowski, Marcus R, Bressem, Keno K, Adams, Lisa C
Publikováno v:
In European Journal of Radiology January 2025 182
