| Artificial intelligence-based pathology as a biomarker of sensitivity to atezolizumab–bevacizumab in patients with hepatocellular carcinoma: a multicentre retrospective study Q Zeng, C Klein, S Caruso, P Maille, DS Allende, B Mínguez, M Iavarone, ... The Lancet Oncology 24 (12), 1411-1422, 2023 | 43 | 2023 |
| Regression-based Deep-Learning predicts molecular biomarkers from pathology slides OSM El Nahhas, CML Loeffler, ZI Carrero, M van Treeck, FR Kolbinger, ... nature communications 15 (1), 1253, 2024 | 37 | 2024 |
| In-context learning enables multimodal large language models to classify cancer pathology images D Ferber, G Wölflein, IC Wiest, M Ligero, S Sainath, N Ghaffari Laleh, ... Nature Communications 15 (1), 10104, 2024 | 16 | 2024 |
| From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology OSM El Nahhas, M van Treeck, G Wölflein, M Unger, M Ligero, T Lenz, ... Nature Protocols 20 (1), 293-316, 2025 | 13 | 2025 |
| Prediction of homologous recombination deficiency from routine histology with attention-based multiple instance learning in nine different tumor types CML Loeffler, OSM El Nahhas, HS Muti, ZI Carrero, T Seibel, ... BMC biology 22 (1), 225, 2024 | 13* | 2024 |
| Autonomous artificial intelligence agents for clinical decision making in oncology D Ferber, OSM El Nahhas, G Wölflein, IC Wiest, J Clusmann, ME Leßman, ... arXiv preprint arXiv:2404.04667, 2024 | 10 | 2024 |
| Benchmarking foundation models as feature extractors for weakly-supervised computational pathology P Neidlinger, OSM El Nahhas, HS Muti, T Lenz, M Hoffmeister, H Brenner, ... arXiv preprint arXiv:2408.15823, 2024 | 8 | 2024 |
| Direct image to subtype prediction for brain tumors using deep learning KJ Hewitt, CML Löffler, HS Muti, AS Berghoff, C Eisenlöffel, M van Treeck, ... Neuro-oncology advances 5 (1), vdad139, 2023 | 8 | 2023 |
| A Good Feature Extractor Is All You Need for Weakly Supervised Learning in Histopathology G Wölflein, D Ferber, AR Meneghetti, OSM El Nahhas, D Truhn, ... arXiv preprint arXiv:2311.11772, 2023 | 6 | 2023 |
| Weakly supervised deep learning predicts immunotherapy response in solid tumors based on PD-L1 expression M Ligero, G Serna, OSM El Nahhas, I Sansano, S Mauchanski, ... Cancer Research Communications 4 (1), 92-102, 2024 | 5 | 2024 |
| Benchmarking pathology feature extractors for whole slide image classification G Wölflein, D Ferber, AR Meneghetti, OSM El Nahhas, D Truhn, ... Preprint at https://arxiv. org/abs/2311.11772, 2023 | 5 | 2023 |
| Multimodal histopathologic models stratify hormone receptor-positive early breast cancer KM Boehm, OSM El Nahhas, A Marra, P Selenica, HY Wen, B Weigelt, ... BioRxiv, 2024.02. 23.581806, 2024 | 3 | 2024 |
| Joint multi-task learning improves weakly-supervised biomarker prediction in computational pathology OSM El Nahhas, G Wölflein, M Ligero, T Lenz, M van Treeck, F Khader, ... International Conference on Medical Image Computing and Computer-Assisted …, 2024 | 2 | 2024 |
| Histopathological evaluation of abdominal aortic aneurysms with deep learning FR Kolbinger, OSM El Nahhas, MC Nackenhorst, C Brostjan, W Eilenberg, ... medRxiv, 2024 | 1 | 2024 |
| Artificial intelligence-based biomarkers for treatment decisions in oncology M Ligero, OSM El Nahhas, M Aldea, JN Kather Trends in Cancer, 2025 | | 2025 |
| Abnormality-Driven Representation Learning for Radiology Imaging M Ligero, T Lenz, G Wölflein, OSM El Nahhas, D Truhn, JN Kather arXiv preprint arXiv:2411.16803, 2024 | | 2024 |
| Compute-Efficient Medical Image Classification with Softmax-Free Transformers and Sequence Normalization F Khader, OSM El Nahhas, T Han, G Müller-Franzes, S Nebelung, ... arXiv preprint arXiv:2406.01314, 2024 | | 2024 |
| Abstract LB386: Weakly-supervised prediction of tumor infiltrating lymphocytes per high power field from colorectal cancer histopathology slides using regression transformers OSE Nahhas, JD Bonner, JK Greenson, DB Schmolze, L Shaktah, ... Cancer Research 84 (7_Supplement), LB386-LB386, 2024 | | 2024 |
| Abstract LB384: Artificial intelligence measures of tumor infiltrating lymphocytes predict colorectal cancer-specific and overall survival SB Gruber, OSE Nahhas, JD Bonner, JK Greenson, D Schmolze, ... Cancer Research 84 (7_Supplement), LB384-LB384, 2024 | | 2024 |
| Simultaneous prediction of tumor microenvironment biomarkers from pathology slides using multi-task deep regression OSE Nahhas, M Ligero, JN Kather Cancer Research 84 (6_Supplement), 6191-6191, 2024 | | 2024 |
Jakob Nikolas Kather, MD, MScElse Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, GermanyVerified email at tu-dresden.de