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Omar S M El Nahhas

Showing results (1-10 of 15) with videos related to

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Trends in Cancer|January 15, 2025
Artificial intelligence-based biomarkers for treatment decisions in oncologyMarta Ligero, Omar S M El Nahhas, Mihaela Aldea, et al.
Diagnostic Pathology|September 15, 2025
Histopathological evaluation of abdominal aortic aneurysms with deep learningFiona R Kolbinger, Omar S M El Nahhas, Maja Carina Nackenhorst, et al.
Medrxiv : the Preprint Server for Health Sciences|May 7, 2024
Histopathological evaluation of abdominal aortic aneurysms with deep learningFiona R Kolbinger, Omar S M El Nahhas, Maja Carina Nackenhorst, et al.
Nature Communications|November 21, 2024
In-context learning enables multimodal large language models to classify cancer pathology imagesDyke Ferber, Georg Wölflein, Isabella C Wiest, et al.
Nature Protocols|September 16, 2024
From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathologyOmar S M El Nahhas, Marko van Treeck, Georg Wölflein, et al.
Nature Cancer|June 6, 2025
Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncologyDyke Ferber, Omar S M El Nahhas, Georg Wölflein, et al.
Nature Biomedical Engineering|October 1, 2025
Benchmarking foundation models as feature extractors for weakly supervised computational pathologyPeter Neidlinger, Omar S M El Nahhas, Hannah Sophie Muti, et al.
Cancer Discovery|March 26, 2026
Machine learning predicts hepatocellular carcinoma risk from routine clinical data: a large population-based multicentric studyJan Clusmann, Paul-Henry Koop, David Y Zhang, et al.
Medrxiv : the Preprint Server for Health Sciences|March 22, 2023
Direct prediction of Homologous Recombination Deficiency from routine histology in ten different tumor types with attention-based Multiple Instance Learning: a development and validation studyChiara Maria Lavinia Loeffler, Omar S M El Nahhas, Hannah Sophie Muti, et al.
BMC Biology|October 8, 2024
Prediction of homologous recombination deficiency from routine histology with attention-based multiple instance learning in nine different tumor typesChiara Maria Lavinia Loeffler, Omar S M El Nahhas, Hannah Sophie Muti, et al.
Pageof 2

Showing results (1-10 of 15) with videos related to

Sort By:
Pageof 2
Trends in Cancer|January 15, 2025
Artificial intelligence-based biomarkers for treatment decisions in oncologyMarta Ligero, Omar S M El Nahhas, Mihaela Aldea, et al.
Diagnostic Pathology|September 15, 2025
Histopathological evaluation of abdominal aortic aneurysms with deep learningFiona R Kolbinger, Omar S M El Nahhas, Maja Carina Nackenhorst, et al.
Medrxiv : the Preprint Server for Health Sciences|May 7, 2024
Histopathological evaluation of abdominal aortic aneurysms with deep learningFiona R Kolbinger, Omar S M El Nahhas, Maja Carina Nackenhorst, et al.
Nature Communications|November 21, 2024
In-context learning enables multimodal large language models to classify cancer pathology imagesDyke Ferber, Georg Wölflein, Isabella C Wiest, et al.
Nature Protocols|September 16, 2024
From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathologyOmar S M El Nahhas, Marko van Treeck, Georg Wölflein, et al.
Nature Cancer|June 6, 2025
Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncologyDyke Ferber, Omar S M El Nahhas, Georg Wölflein, et al.
Nature Biomedical Engineering|October 1, 2025
Benchmarking foundation models as feature extractors for weakly supervised computational pathologyPeter Neidlinger, Omar S M El Nahhas, Hannah Sophie Muti, et al.
Cancer Discovery|March 26, 2026
Machine learning predicts hepatocellular carcinoma risk from routine clinical data: a large population-based multicentric studyJan Clusmann, Paul-Henry Koop, David Y Zhang, et al.
Medrxiv : the Preprint Server for Health Sciences|March 22, 2023
Direct prediction of Homologous Recombination Deficiency from routine histology in ten different tumor types with attention-based Multiple Instance Learning: a development and validation studyChiara Maria Lavinia Loeffler, Omar S M El Nahhas, Hannah Sophie Muti, et al.
BMC Biology|October 8, 2024
Prediction of homologous recombination deficiency from routine histology with attention-based multiple instance learning in nine different tumor typesChiara Maria Lavinia Loeffler, Omar S M El Nahhas, Hannah Sophie Muti, et al.
Pageof 2