Omar S. M. El Nahhas

According to our database, Omar S. M. El Nahhas authored at least 6 papers between 2023 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

In-context learning enables multimodal large language models to classify cancer pathology images.

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

CoRR, 2024

Reducing self-supervised learning complexity improves weakly-supervised classification performance in computational pathology.

Tim Lenz, Omar S. M. El Nahhas, Marta Ligero, Jakob Nikolas Kather

CoRR, 2024

Joint multi-task learning improves weakly-supervised biomarker prediction in computational pathology.

Omar S. M. El Nahhas, Georg Wölflein, Marta Ligero, Tim Lenz, Marko van Treeck, Firas Khader, Daniel Truhn, Jakob Nikolas Kather

CoRR, 2024

2023

From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology.

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

CoRR, 2023

A Good Feature Extractor Is All You Need for Weakly Supervised Learning in Histopathology.

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

CoRR, 2023

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides.

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

CoRR, 2023