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2020 – today
- 2024
[c2]Omar S. M. El Nahhas, Georg Wölflein, Marta Ligero, Tim Lenz, Marko van Treeck, Firas Khader, Daniel Truhn, Jakob Nikolas Kather:
Joint Multi-task Learning Improves Weakly-Supervised Biomarker Prediction in Computational Pathology. MICCAI (4) 2024: 254-262- [i4]Omar S. M. El Nahhas, Georg Wölflein, Marta Ligero, Tim Lenz, Marko van Treeck, Firas Khader, Daniel Truhn, Jakob Nikolas Kather:
Joint multi-task learning improves weakly-supervised biomarker prediction in computational pathology. CoRR abs/2403.03891 (2024) - [i3]Tim Lenz, Omar S. M. El Nahhas, Marta Ligero, Jakob Nikolas Kather:
Reducing self-supervised learning complexity improves weakly-supervised classification performance in computational pathology. CoRR abs/2403.04558 (2024) - [i2]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:
In-context learning enables multimodal large language models to classify cancer pathology images. CoRR abs/2403.07407 (2024) - 2023
- [i1]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:
From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology. CoRR abs/2312.10944 (2023)
2010 – 2019
- 2019
- [c1]Marta Ligero
, Guillermo Torres, Carles Sánchez, Katerine Díaz-Chito, Raquel Perez, Debora Gil:
Selection of Radiomics Features based on their Reproducibility. EMBC 2019: 403-408
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