Omar S. M. El Nahhas

Publication Activity (10 Years)

Years Active: 2023-2024
Publications (10 Years): 8

Top Topics

Weakly Supervised

Top Venues

Publications

Omar S. M. El Nahhas, Georg Wölflein, Marta Ligero, Tim Lenz, Marko van Treeck, Firas Khader, Daniel Truhn, Jakob Nikolas Kather
Joint multi-task learning improves weakly-supervised biomarker prediction in computational pathology. CoRR (2024)
Firas Khader, Omar S. M. El Nahhas, Tianyu Han, Gustav Müller-Franzes, Sven Nebelung, Jakob Nikolas Kather, Daniel Truhn
Compute-Efficient Medical Image Classification with Softmax-Free Transformers and Sequence Normalization. CoRR (2024)
Dyke Ferber, Omar S. M. El Nahhas, Georg Wölflein, Isabella C. Wiest, Jan Clusmann, Marie-Elisabeth Leßman, Sebastian Foersch, Jacqueline Lammert, Maximilian Tschochohei, Dirk Jäger, Manuel Salto-Tellez, Nikolaus Schultz, Daniel Truhn, Jakob Nikolas Kather
Autonomous Artificial Intelligence Agents for Clinical Decision Making in Oncology. CoRR (2024)
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
In-context learning enables multimodal large language models to classify cancer pathology images. CoRR (2024)
Tim Lenz, Omar S. M. El Nahhas, Marta Ligero, Jakob Nikolas Kather
Reducing self-supervised learning complexity improves weakly-supervised classification performance in computational pathology. CoRR (2024)
Georg Wölflein, Dyke Ferber, Asier Rabasco Meneghetti, Omar S. M. El Nahhas, Daniel Truhn, Zunamys I. Carrero, David J. Harrison, Ognjen Arandjelovic, Jakob Nikolas Kather
A Good Feature Extractor Is All You Need for Weakly Supervised Learning in Histopathology. CoRR (2023)
Omar S. M. El Nahhas, Chiara Maria Lavinia Loeffler, Zunamys I. Carrero, Marko van Treeck, Fiona R. Kolbinger, Katherine Jane Hewitt, Hannah Sophie Muti, Mara Graziani, Qinghe Zeng, Julien Calderaro, Nadina Ortiz-Brüchle, Tanwei Yuan, Michael Hoffmeister, Hermann Brenner, Alexander Brobeil, Jorge S. Reis-Filho, Jakob Nikolas Kather
Regression-based Deep-Learning predicts molecular biomarkers from pathology slides. CoRR (2023)
Omar S. M. El Nahhas, Marko van Treeck, Georg Wölflein, Michaela Unger, Marta Ligero, Tim Lenz, Sophia J. Wagner, Katherine Jane Hewitt, Firas Khader, Sebastian Foersch, Daniel Truhn, Jakob Nikolas Kather
From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology. CoRR (2023)