Deep Learning for interpretable end-to-end survival (E-ESurv) prediction in gastrointestinal cancer histopathology. - Narmin Ghaffari Laleh; Amelie Echle; Hannah Sophie Muti; Katherine Jane Hewitt; Volkmar Schulz; Jakob Nikolas Kather (COMPAY@MICCAI, 2021)

Test Time Transform Prediction for Open Set Histopathological Image Recognition. - Adrian Galdran; Katherine Jane Hewitt; Narmin Ghaffari Laleh; Jakob Nikolas Kather; Gustavo Carneiro 001; Miguel Ángel González Ballester (MICCAI (2), 2022)

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)

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)

Using histopathology latent diffusion models as privacy-preserving dataset augmenters improves downstream classification performance. - Jan Moritz Niehues; Gustav Müller-Franzes; Yoni Schirris; Sophia J. Wagner; Michael Jendrusch; Matthias Kloor; Alexander T. Pearson; Hannah Sophie Muti; Katherine Jane Hewitt; Gregory Patrick Veldhuizen; Laura Zigutyte; Daniel Truhn; et al. (Comput. Biol. Medicine, 2024)