Selection of Radiomics Features based on their Reproducibility. - Marta Ligero; Guillermo Torres; Carles Sánchez; Katerine Díaz-Chito; Raquel Perez; Debora Gil (EMBC, 2019)

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)

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)

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)

Unsupervised Foundation Model-Agnostic Slide-Level Representation Learning. - Tim Lenz; Peter Neidlinger; Marta Ligero; Georg Wölflein; Marko van Treeck; Jakob Nikolas Kather (CVPR, 2025)

Three-dimensional end-to-end deep learning for brain MRI analysis. - 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 (CoRR, 2025)