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; et al. (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)

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 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 (ECCV Workshops (16), 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 (MICCAI (4), 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)

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)

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; et al. (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)

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; et al. (CoRR, 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)

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 (ISBI, 2025)