Výsledky vyhledávání - "Makowski, Marcus R"

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Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Data

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

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Influence of Medical Foreign Bodies on Dark-Field Chest Radiographs: First experiences

Autor: Kaster, Lennard, Klein, Henriette, Marka, Alexander W., Urban, Theresa, Karl, Sandra, Gassert, Florian T., Steinhelfer, Lisa, Makowski, Marcus R., Pfeiffer, Daniela, Pfeiffer, Franz

Objectives: Evaluating the effects and artifacts introduced by medical foreign bodies in clinical dark-field chest radiographs and assessing their influence on the evaluation of pulmonary tissue, compared to conventional radiographs. Material & Metho

Externí odkaz: http://arxiv.org/abs/2408.10855

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Incorporating Anatomical Awareness for Enhanced Generalizability and Progression Prediction in Deep Learning-Based Radiographic Sacroiliitis Detection

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

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MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT

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

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MEDBERT.de: A Comprehensive German BERT Model for the Medical Domain

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

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What Does DALL-E 2 Know About Radiology?

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

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