Search Results for author: Omar S. M. El Nahhas

Found 8 papers, 4 papers with code

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

no code implementations7 Mar 2024 Tim Lenz, Omar S. M. El Nahhas, Marta Ligero, Jakob Nikolas Kather

Specifically, we analyzed the effects of adaptations in data volume, architecture, and algorithms on downstream classification tasks, emphasizing their impact on computational resources.

Classification Self-Supervised Learning +1

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

1 code implementation11 Apr 2023 Omar S. M. El Nahhas, Chiara M. L. Loeffler, Zunamys I. Carrero, Marko van Treeck, Fiona R. Kolbinger, Katherine J. Hewitt, Hannah S. 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

We tested our method for multiple clinically and biologically relevant biomarkers: homologous repair deficiency (HRD) score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment.

Classification regression

Cannot find the paper you are looking for? You can Submit a new open access paper.