Search Results - "Truhn, Daniel"

Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI by Truhn, Daniel, Schrading, Simone, Haarburger, Christoph, Schneider, Hannah, Merhof, Dorit, Kuhl, Christiane

“…Purpose To compare the diagnostic performance of radiomic analysis (RA) and a convolutional neural network (CNN) to radiologists for classification of contrast…”
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Adversarial attacks and adversarial robustness in computational pathology by Ghaffari Laleh, Narmin, Truhn, Daniel, Veldhuizen, Gregory Patrick, Han, Tianyu, van Treeck, Marko, Buelow, Roman D., Langer, Rupert, Dislich, Bastian, Boor, Peter, Schulz, Volkmar, Kather, Jakob Nikolas

“…Artificial Intelligence (AI) can support diagnostic workflows in oncology by aiding diagnosis and providing biomarkers directly from routine pathology slides…”
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Large language models streamline automated machine learning for clinical studies by Tayebi Arasteh, Soroosh, Han, Tianyu, Lotfinia, Mahshad, Kuhl, Christiane, Kather, Jakob Nikolas, Truhn, Daniel, Nebelung, Sven

“…A knowledge gap persists between machine learning (ML) developers (e.g., data scientists) and practitioners (e.g., clinicians), hampering the full utilization…”
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A multimodal comparison of latent denoising diffusion probabilistic models and generative adversarial networks for medical image synthesis by Müller-Franzes, Gustav, Niehues, Jan Moritz, Khader, Firas, Arasteh, Soroosh Tayebi, Haarburger, Christoph, Kuhl, Christiane, Wang, Tianci, Han, Tianyu, Nolte, Teresa, Nebelung, Sven, Kather, Jakob Nikolas, Truhn, Daniel

“…Although generative adversarial networks (GANs) can produce large datasets, their limited diversity and fidelity have been recently addressed by denoising…”
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Correction: Integrating Text and Image Analysis: Exploring GPT-4V's Capabilities in Advanced Radiological Applications Across Subspecialties by Busch, Felix, Han, Tianyu, Makowski, Marcus R, Truhn, Daniel, Bressem, Keno K, Adams, Lisa

“…[This corrects the article DOI: 10.2196/54948.]…”
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Multimodal Deep Learning for Integrating Chest Radiographs and Clinical Parameters: A Case for Transformers by Khader, Firas, Müller-Franzes, Gustav, Wang, Tianci, Han, Tianyu, Tayebi Arasteh, Soroosh, Haarburger, Christoph, Stegmaier, Johannes, Bressem, Keno, Kuhl, Christiane, Nebelung, Sven, Kather, Jakob Nikolas, Truhn, Daniel

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Medical domain knowledge in domain-agnostic generative AI by Kather, Jakob Nikolas, Ghaffari Laleh, Narmin, Foersch, Sebastian, Truhn, Daniel

“…The text-guided diffusion model GLIDE (Guided Language to Image Diffusion for Generation and Editing) is the state of the art in text-to-image generative…”
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A pilot study on the efficacy of GPT-4 in providing orthopedic treatment recommendations from MRI reports by Truhn, Daniel, Weber, Christian D., Braun, Benedikt J., Bressem, Keno, Kather, Jakob N., Kuhl, Christiane, Nebelung, Sven

“…Large language models (LLMs) have shown potential in various applications, including clinical practice. However, their accuracy and utility in providing…”
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Denoising diffusion probabilistic models for 3D medical image generation by Khader, Firas, Müller-Franzes, Gustav, Tayebi Arasteh, Soroosh, Han, Tianyu, Haarburger, Christoph, Schulze-Hagen, Maximilian, Schad, Philipp, Engelhardt, Sandy, Baeßler, Bettina, Foersch, Sebastian, Stegmaier, Johannes, Kuhl, Christiane, Nebelung, Sven, Kather, Jakob Nikolas, Truhn, Daniel

“…Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models have generated realistic images from…”
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Enhancing domain generalization in the AI-based analysis of chest radiographs with federated learning by Tayebi Arasteh, Soroosh, Kuhl, Christiane, Saehn, Marwin-Jonathan, Isfort, Peter, Truhn, Daniel, Nebelung, Sven

“…Developing robust artificial intelligence (AI) models that generalize well to unseen datasets is challenging and usually requires large and variable datasets,…”
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What Does DALL-E 2 Know About Radiology? by Adams, Lisa C, Busch, Felix, Truhn, Daniel, Makowski, Marcus R, Aerts, Hugo J W L, Bressem, Keno K

“…Generative models, such as DALL-E 2 (OpenAI), could represent promising future tools for image generation, augmentation, and manipulation for artificial…”
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Integrating Text and Image Analysis: Exploring GPT-4V's Capabilities in Advanced Radiological Applications Across Subspecialties by Busch, Felix, Han, Tianyu, Makowski, Marcus R, Truhn, Daniel, Bressem, Keno K, Adams, Lisa

“…This study demonstrates that GPT-4V outperforms GPT-4 across radiology subspecialties in analyzing 207 cases with 1312 images from the Radiological Society of…”
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Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization by Han, Tianyu, Nebelung, Sven, Pedersoli, Federico, Zimmermann, Markus, Schulze-Hagen, Maximilian, Ho, Michael, Haarburger, Christoph, Kiessling, Fabian, Kuhl, Christiane, Schulz, Volkmar, Truhn, Daniel

“…Unmasking the decision making process of machine learning models is essential for implementing diagnostic support systems in clinical practice. Here, we…”
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Medical transformer for multimodal survival prediction in intensive care: integration of imaging and non-imaging data by Khader, Firas, Kather, Jakob Nikolas, Müller-Franzes, Gustav, Wang, Tianci, Han, Tianyu, Tayebi Arasteh, Soroosh, Hamesch, Karim, Bressem, Keno, Haarburger, Christoph, Stegmaier, Johannes, Kuhl, Christiane, Nebelung, Sven, Truhn, Daniel

“…When clinicians assess the prognosis of patients in intensive care, they take imaging and non-imaging data into account. In contrast, many traditional machine…”
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Fibroglandular tissue segmentation in breast MRI using vision transformers: a multi-institutional evaluation by Müller-Franzes, Gustav, Müller-Franzes, Fritz, Huck, Luisa, Raaff, Vanessa, Kemmer, Eva, Khader, Firas, Arasteh, Soroosh Tayebi, Lemainque, Teresa, Kather, Jakob Nikolas, Nebelung, Sven, Kuhl, Christiane, Truhn, Daniel

“…Accurate and automatic segmentation of fibroglandular tissue in breast MRI screening is essential for the quantification of breast density and background…”
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Collaborative training of medical artificial intelligence models with non-uniform labels by Tayebi Arasteh, Soroosh, Isfort, Peter, Saehn, Marwin, Mueller-Franzes, Gustav, Khader, Firas, Kather, Jakob Nikolas, Kuhl, Christiane, Nebelung, Sven, Truhn, Daniel

“…Due to the rapid advancements in recent years, medical image analysis is largely dominated by deep learning (DL). However, building powerful and robust DL…”
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Preoperative three-dimensional lung volumetry predicts respiratory complications in patients undergoing major liver resection for colorectal metastases by Elmaagacli, Suzan, Thiele, Christoph, Meister, Franziska, Menne, Philipp, Truhn, Daniel, Olde Damink, Steven W. M., Bickenbach, Johannes, Neumann, Ulf, Lang, Sven Arke, Vondran, Florian, Amygdalos, Iakovos

“…Colorectal liver metastases (CRLM) are the predominant factor limiting survival in patients with colorectal cancer and liver resection with complete tumor…”
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Novel machine learning algorithm can identify patients at risk of poor overall survival following curative resection for colorectal liver metastases by Amygdalos, Iakovos, Müller‐Franzes, Gustav, Bednarsch, Jan, Czigany, Zoltan, Ulmer, Tom Florian, Bruners, Philipp, Kuhl, Christiane, Neumann, Ulf Peter, Truhn, Daniel, Lang, Sven Arke

“…Background/Purpose The primary cause of mortality in colorectal cancer is metastatic disease. We investigated the ability of a machine learning (ML) algorithm…”
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Author Correction: A pilot study on the efficacy of GPT-4 in providing orthopedic treatment recommendations from MRI reports by Truhn, Daniel, Weber, Christian D., Braun, Benedikt J., Bressem, Keno, Kather, Jakob N., Kuhl, Christiane, Nebelung, Sven

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Large language models and multimodal foundation models for precision oncology by Truhn, Daniel, Eckardt, Jan-Niklas, Ferber, Dyke, Kather, Jakob Nikolas

“…The technological progress in artificial intelligence (AI) has massively accelerated since 2022, with far-reaching implications for oncology and cancer…”
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