Zobrazeno 1 - 10
of 140
pro vyhledávání: '"Omar, S. M."'
Autor:
Ligero, Marta, Lenz, Tim, Wölflein, Georg, Nahhas, Omar S. M. El, Truhn, Daniel, Kather, Jakob Nikolas
To date, the most common approach for radiology deep learning pipelines is the use of end-to-end 3D networks based on models pre-trained on other tasks, followed by fine-tuning on the task at hand. In contrast, adjacent medical fields such as patholo
Externí odkaz:
http://arxiv.org/abs/2411.16803
Autor:
Neidlinger, Peter, Nahhas, Omar S. M. El, Muti, Hannah Sophie, Lenz, Tim, Hoffmeister, Michael, Brenner, Hermann, van Treeck, Marko, Langer, Rupert, Dislich, Bastian, Behrens, Hans Michael, Röcken, Christoph, Foersch, Sebastian, Truhn, Daniel, Marra, Antonio, Saldanha, Oliver Lester, Kather, Jakob Nikolas
Advancements in artificial intelligence have driven the development of numerous pathology foundation models capable of extracting clinically relevant information. However, there is currently limited literature independently evaluating these foundatio
Externí odkaz:
http://arxiv.org/abs/2408.15823
Autor:
Khader, Firas, Nahhas, Omar S. M. El, Han, Tianyu, Müller-Franzes, Gustav, Nebelung, Sven, Kather, Jakob Nikolas, Truhn, Daniel
The Transformer model has been pivotal in advancing fields such as natural language processing, speech recognition, and computer vision. However, a critical limitation of this model is its quadratic computational and memory complexity relative to the
Externí odkaz:
http://arxiv.org/abs/2406.01314
Autor:
Ferber, Dyke, Nahhas, Omar S. M. El, Wölflein, Georg, Wiest, Isabella C., Clusmann, Jan, Leßman, Marie-Elisabeth, Foersch, Sebastian, Lammert, Jacqueline, Tschochohei, Maximilian, Jäger, Dirk, Salto-Tellez, Manuel, Schultz, Nikolaus, Truhn, Daniel, Kather, Jakob Nikolas
Multimodal artificial intelligence (AI) systems have the potential to enhance clinical decision-making by interpreting various types of medical data. However, the effectiveness of these models across all medical fields is uncertain. Each discipline p
Externí odkaz:
http://arxiv.org/abs/2404.04667
Autor:
Ferber, Dyke, Wölflein, Georg, Wiest, Isabella C., Ligero, Marta, Sainath, Srividhya, Laleh, Narmin Ghaffari, Nahhas, Omar S. M. El, Müller-Franzes, Gustav, Jäger, Dirk, Truhn, Daniel, Kather, Jakob Nikolas
Medical image classification requires labeled, task-specific datasets which are used to train deep learning networks de novo, or to fine-tune foundation models. However, this process is computationally and technically demanding. In language processin
Externí odkaz:
http://arxiv.org/abs/2403.07407
Deep Learning models have been successfully utilized to extract clinically actionable insights from routinely available histology data. Generally, these models require annotations performed by clinicians, which are scarce and costly to generate. The
Externí odkaz:
http://arxiv.org/abs/2403.04558
Joint multi-task learning improves weakly-supervised biomarker prediction in computational pathology
Autor:
Nahhas, Omar S. M. El, Wölflein, Georg, Ligero, Marta, Lenz, Tim, van Treeck, Marko, Khader, Firas, Truhn, Daniel, Kather, Jakob Nikolas
Deep Learning (DL) can predict biomarkers directly from digitized cancer histology in a weakly-supervised setting. Recently, the prediction of continuous biomarkers through regression-based DL has seen an increasing interest. Nonetheless, clinical de
Externí odkaz:
http://arxiv.org/abs/2403.03891
Autor:
Nahhas, Omar S. M. El, van Treeck, Marko, Wölflein, Georg, Unger, Michaela, Ligero, Marta, Lenz, Tim, Wagner, Sophia J., Hewitt, Katherine J., Khader, Firas, Foersch, Sebastian, Truhn, Daniel, Kather, Jakob Nikolas
Hematoxylin- and eosin (H&E) stained whole-slide images (WSIs) are the foundation of diagnosis of cancer. In recent years, development of deep learning-based methods in computational pathology enabled the prediction of biomarkers directly from WSIs.
Externí odkaz:
http://arxiv.org/abs/2312.10944
Autor:
Wölflein, Georg, Ferber, Dyke, Meneghetti, Asier R., Nahhas, Omar S. M. El, Truhn, Daniel, Carrero, Zunamys I., Harrison, David J., Arandjelović, Ognjen, Kather, Jakob Nikolas
Weakly supervised whole slide image classification is a key task in computational pathology, which involves predicting a slide-level label from a set of image patches constituting the slide. Constructing models to solve this task involves multiple de
Externí odkaz:
http://arxiv.org/abs/2311.11772
Autor:
Dyke Ferber, Georg Wölflein, Isabella C. Wiest, Marta Ligero, Srividhya Sainath, Narmin Ghaffari Laleh, Omar S. M. El Nahhas, Gustav Müller-Franzes, Dirk Jäger, Daniel Truhn, Jakob Nikolas Kather
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract Medical image classification requires labeled, task-specific datasets which are used to train deep learning networks de novo, or to fine-tune foundation models. However, this process is computationally and technically demanding. In language
Externí odkaz:
https://doaj.org/article/3fa7eb85942f4c83b6c9d066cad3cbe7
