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ORCIDTim Lenz
Publications
- 2025
[c3]Tim Lenz, Peter Neidlinger, Marta Ligero, Georg Wölflein, Marko van Treeck, Jakob Nikolas Kather:
Unsupervised Foundation Model-Agnostic Slide-Level Representation Learning. CVPR 2025: 30807-30817- [c2]Marta Ligero, Tim Lenz, Georg Wölflein
, Omar S. M. El Nahhas, Daniel Truhn, Jakob Nikolas Kather:
Abnormality-Driven Representation Learning for Radiology Imaging. MICCAI (4) 2025: 14-24 - [i8]Radhika Juglan, Marta Ligero, Zunamys I. Carrero, Asier Rabasco Meneghetti, Tim Lenz, Leo Misera, Gregory Patrick Veldhuizen, Paul Kuntke, Hagen H. Kitzler, Sven Nebelung, Daniel Truhn, Jakob Nikolas Kather:
Three-dimensional end-to-end deep learning for brain MRI analysis. CoRR abs/2506.23916 (2025) - 2024
- [c1]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 - [i7]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) - [i6]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) - [i3]Tim Lenz, Peter Neidlinger, Marta Ligero, Georg Wölflein, Marko van Treeck, Jakob Nikolas Kather:
Unsupervised Foundation Model-Agnostic Slide-Level Representation Learning. CoRR abs/2411.13623 (2024) - [i2]Marta Ligero, Tim Lenz, Georg Wölflein, Omar S. M. El Nahhas, Daniel Truhn, Jakob Nikolas Kather:
Abnormality-Driven Representation Learning for Radiology Imaging. CoRR abs/2411.16803 (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)
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