Omar S. M. El Nahhas

According to our database, Omar S. M. El Nahhas authored at least 12 papers between 2023 and 2025.

Collaborative distances:

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

2025

Diagnosis with Nanoscale Protein Distributions: Single-Molecule Fluorescence Localization Microscopy and Attention-Based Learning.

Alistair Curd, Hayley Slaney, Scarlet F. Brockmöller, Andras-Gabor Miklosi, Deep Arora, Andrew Lewington, James H. Felce, Omar S. M. El Nahhas, Marko van Treeck, Jakob Nikolas Kather, Phil Quirke

Proceedings of the 22nd IEEE International Symposium on Biomedical Imaging, 2025

2024

Abnormality-Driven Representation Learning for Radiology Imaging.

Marta Ligero, Tim Lenz, Georg Wölflein, Omar S. M. El Nahhas, Daniel Truhn, Jakob Nikolas Kather

CoRR, 2024

Benchmarking foundation models as feature extractors for weakly-supervised computational pathology.

Peter Neidlinger, Omar S. M. El Nahhas, Hannah Sophie Muti, Tim Lenz, Michael Hoffmeister, Hermann Brenner, Marko van Treeck, Rupert Langer, Bastian Dislich, Hans-Michael Behrens, Christoph Röcken, Sebastian Foersch, Daniel Truhn, Antonio Marra, Oliver Lester Saldanha, Jakob Nikolas Kather

CoRR, 2024

Compute-Efficient Medical Image Classification with Softmax-Free Transformers and Sequence Normalization.

Firas Khader, Omar S. M. El Nahhas, Tianyu Han, Gustav Müller-Franzes, Sven Nebelung, Jakob Nikolas Kather, Daniel Truhn

CoRR, 2024

Autonomous Artificial Intelligence Agents for Clinical Decision Making in Oncology.

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

CoRR, 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

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2024, 2024

A Good Feature Extractor Is All You Need for Weakly Supervised Pathology Slide Classification.

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

Proceedings of the Computer Vision - ECCV 2024 Workshops, 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