Search Results for author: Keno K. Bressem

Found 11 papers, 3 papers with code

Is Open-Source There Yet? A Comparative Study on Commercial and Open-Source LLMs in Their Ability to Label Chest X-Ray Reports

no code implementations19 Feb 2024 Felix J. Dorfner, Liv Jürgensen, Leonhard Donle, Fares Al Mohamad, Tobias R. Bodenmann, Mason C. Cleveland, Felix Busch, Lisa C. Adams, James Sato, Thomas Schultz, Albert E. Kim, Jameson Merkow, Keno K. Bressem, Christopher P. Bridge

While recent publications have explored GPT-4 in its application to extracting information of interest from radiology reports, there has not been a real-world comparison of GPT-4 to different leading open-source models.

Privacy Preserving

MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data

no code implementations14 Apr 2023 Tianyu Han, Lisa C. Adams, Jens-Michalis Papaioannou, Paul Grundmann, Tom Oberhauser, Alexander Löser, Daniel Truhn, Keno K. Bressem

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.

What Does DALL-E 2 Know About Radiology?

no code implementations27 Sep 2022 Lisa C. Adams, Felix Busch, Daniel Truhn, Marcus R. Makowski, Hugo JWL. Aerts, Keno K. Bressem

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.

Zero-Shot Text-to-Image Generation

3D U-Net for segmentation of COVID-19 associated pulmonary infiltrates using transfer learning: State-of-the-art results on affordable hardware

no code implementations25 Jan 2021 Keno K. Bressem, Stefan M. Niehues, Bernd Hamm, Marcus R. Makowski, Janis L. Vahldiek, Lisa C. Adams

Our model performed comparable to previously published 3D U-Net architectures, achieving a mean Dice score of 0. 679 on the tuning dataset, 0. 648 on the Coronacases dataset and 0. 405 on the MosMed dataset.

Computed Tomography (CT) Segmentation +1

Comparing Different Deep Learning Architectures for Classification of Chest Radiographs

no code implementations20 Feb 2020 Keno K. Bressem, Lisa Adams, Christoph Erxleben, Bernd Hamm, Stefan Niehues, Janis Vahldiek

Chest radiographs are among the most frequently acquired images in radiology and are often the subject of computer vision research.

Classification Deep Learning +1

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