Dimensionality Reduction Strategies for CNN-Based Classification of Histopathological Images. - Silvia Cascianelli; Raquel Bello-Cerezo; Francesco Bianconi; Mario Luca Fravolini; Mehdi Belal; Barbara Palumbo; Jakob Nikolas Kather (IIMSS, 2017)

Evaluation of Colour Pre-processing on Patch-Based Classification of H&E-Stained Images. - Francesco Bianconi; Jakob Nikolas Kather; Constantino Carlos Reyes-Aldasoro (ECDP, 2019)

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)

Predicting Osteoarthritis Progression in Radiographs via Unsupervised Representation Learning. - Tianyu Han; Jakob Nikolas Kather; Federico Pedersoli; Markus Zimmermann; Sebastian Keil; Maximilian Schulze-Hagen; Marc Terwoelbeck; Peter Isfort; Christoph Haarburger; Fabian Kiessling; Volkmar Schulz; Christiane Kuhl; et al. (CoRR, 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)

Medical Diffusion - Denoising Diffusion Probabilistic Models for 3D Medical Image Generation. - Firas Khader; Gustav Mueller-Franzes; Soroosh Tayebi Arasteh; Tianyu Han; Christoph Haarburger; Maximilian Schulze-Hagen; Philipp Schad; Sandy Engelhardt; Bettina Baeßler; Sebastian Foersch; Johannes Stegmaier; Christiane Kuhl; et al. (CoRR, 2022)

Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels. - Soroosh Tayebi Arasteh; Peter Isfort; Marwin Saehn; Gustav Mueller-Franzes; Firas Khader; Jakob Nikolas Kather; Christiane Kuhl; Sven Nebelung; Daniel Truhn (CoRR, 2022)

Diffusion Probabilistic Models beat GANs on Medical Images. - Gustav Müller-Franzes; Jan Moritz Niehues; Firas Khader; Soroosh Tayebi Arasteh; Christoph Haarburger; Christiane Kuhl; Tianci Wang; Tianyu Han; Sven Nebelung; Jakob Nikolas Kather; Daniel Truhn (CoRR, 2022)

Medical Diagnosis with Large Scale Multimodal Transformers: Leveraging Diverse Data for More Accurate Diagnosis. - Firas Khader; Gustav Mueller-Franzes; Tianci Wang; Tianyu Han; Soroosh Tayebi Arasteh; Christoph Haarburger; Johannes Stegmaier; Keno K. Bressem; Christiane Kuhl; Sven Nebelung; Jakob Nikolas Kather; Daniel Truhn (CoRR, 2022)

Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. - Narmin Ghaffari Laleh; Hannah Sophie Muti; Chiara Maria Lavinia Loeffler; Amelie Echle; Oliver Lester Saldanha; Faisal Mahmood 001; Ming Y. Lu; Christian Trautwein; Rupert Langer; Bastian Dislich; Roman David Bülow; Heike Irmgard Grabsch; et al. (Medical Image Anal., 2022)

Erratum to 'Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology' Medical Image Analysis, Volume 79, July 2022, 102474. - Narmin Ghaffari Laleh; Hannah Sophie Muti; Chiara Maria Lavinia Loeffler; Amelie Echle; Oliver Lester Saldanha; Faisal Mahmood 001; Ming Y. Lu; Christian Trautwein; Rupert Langer; Bastian Dislich; Roman David Bülow; Heike Irmgard Grabsch; et al. (Medical Image Anal., 2022)

Medical domain knowledge in domain-agnostic generative AI. - Jakob Nikolas Kather; Narmin Ghaffari Laleh; Sebastian Foersch; Daniel Truhn (npj Digit. Medicine, 2022)

Classical mathematical models for prediction of response to chemotherapy and immunotherapy. - Narmin Ghaffari Laleh; Chiara Maria Lavinia Loeffler; Julia Grajek; Katerina Stanková; Alexander T. Pearson; Hannah Sophie Muti; Christian Trautwein; Heiko Enderling; Jan Poleszczuk; Jakob Nikolas Kather (PLoS Comput. Biol., 2022)

Image prediction of disease progression for osteoarthritis by style-based manifold extrapolation. - Tianyu Han; Jakob Nikolas Kather; Federico Pedersoli; Markus Zimmermann; Sebastian Keil; Maximilian Schulze-Hagen; Marc Terwoelbeck; Peter Isfort; Christoph Haarburger; Fabian Kiessling; Christiane Kuhl; Volkmar Schulz; et al. (Nat. Mac. Intell., 2022)

Vector-Quantized Latent Flows for Medical Image Synthesis and Out-Of-Distribution Detection. - Firas Khader; Gustav Müller-Franzes; Soroosh Tayebi Arasteh; Tianyu Han; Jakob Nikolas Kather; Johannes Stegmaier; Sven Nebelung; Daniel Truhn (ISBI, 2023)

Cascaded Cross-Attention Networks for Data-Efficient Whole-Slide Image Classification Using Transformers. - Firas Khader; Jakob Nikolas Kather; Tianyu Han; Sven Nebelung; Christiane Kuhl; Johannes Stegmaier; Daniel Truhn (MLMI@MICCAI (2), 2023)

Fully transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study. - Sophia J. Wagner; Daniel Reisenbüchler; Nicholas P. West; Jan Moritz Niehues; Gregory Patrick Veldhuizen; Philip Quirke; Heike Irmgard Grabsch; Piet A. van den Brandt; Gordon G. A. Hutchins; Susan D. Richman; Tanwei Yuan; Rupert Langer; et al. (CoRR, 2023)

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)

Fibroglandular Tissue Segmentation in Breast MRI using Vision Transformers - A multi-institutional evaluation. - Gustav Müller-Franzes; Fritz Müller-Franzes; Luisa Huck; Vanessa Raaff; Eva Kemmer; Firas Khader; Soroosh Tayebi Arasteh; Teresa Nolte; Jakob Nikolas Kather; Sven Nebelung; Christiane Kuhl; Daniel Truhn (CoRR, 2023)

Using Multiple Dermoscopic Photographs of One Lesion Improves Melanoma Classification via Deep Learning: A Prognostic Diagnostic Accuracy Study. - Achim Hekler; Roman C. Maron; Sarah Haggenmüller; Max Schmitt; Christoph Wies; Jochen S. Utikal; Friedegund Meier; Sarah Hobelsberger; Frank Friedrich Gellrich; Mildred Sergon; Axel Hauschild; Lars E. French; et al. (CoRR, 2023)

Enhancing Network Initialization for Medical AI Models Using Large-Scale, Unlabeled Natural Images. - Soroosh Tayebi Arasteh; Leo Misera; Jakob Nikolas Kather; Daniel Truhn; Sven Nebelung (CoRR, 2023)

Empowering Clinicians and Democratizing Data Science: Large Language Models Automate Machine Learning for Clinical Studies. - Soroosh Tayebi Arasteh; Tianyu Han; Mahshad Lotfinia; Christiane Kuhl; Jakob Nikolas Kather; Daniel Truhn; Sven Nebelung (CoRR, 2023)

Medical Foundation Models are Susceptible to Targeted Misinformation Attacks. - Tianyu Han; Sven Nebelung; Firas Khader; Tianci Wang; Gustav Mueller-Franzes; Christiane Kuhl; Sebastian Försch; Jens Kleesiek; Christoph Haarburger; Keno K. Bressem; Jakob Nikolas Kather; Daniel Truhn (CoRR, 2023)

Reconstruction of Patient-Specific Confounders in AI-based Radiologic Image Interpretation using Generative Pretraining. - Tianyu Han; Laura Zigutyte; Luisa Huck; Marc Huppertz; Robert Siepmann; Yossi Gandelsman; Christian Blüthgen; Firas Khader; Christiane Kuhl; Sven Nebelung; Jakob Nikolas Kather; Daniel Truhn (CoRR, 2023)

A Good Feature Extractor Is All You Need for Weakly Supervised Learning in Histopathology. - Georg Wölflein; Dyke Ferber; Asier Rabasco Meneghetti; Omar S. M. El Nahhas; Daniel Truhn; Zunamys I. Carrero; David J. Harrison; Ognjen Arandjelovic; Jakob Nikolas Kather (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)

Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: A systematic review of epigenome-wide studies. - Tanwei Yuan; Dominic Edelmann; Ziwen Fan; Elizabeth Alwers; Jakob Nikolas Kather; Hermann Brenner; Michael Hoffmeister (Artif. Intell. Medicine, 2023)

A Good Feature Extractor Is All You Need for Weakly Supervised Pathology Slide Classification. - Georg Wölflein; Dyke Ferber; Asier Rabasco Meneghetti; Omar S. M. El Nahhas; Daniel Truhn; Zunamys I. Carrero; David J. Harrison; Ognjen Arandjelovic; Jakob Nikolas Kather (ECCV Workshops (16), 2024)

On Instabilities of Unsupervised Denoising Diffusion Models in Magnetic Resonance Imaging Reconstruction. - Tianyu Han; Sven Nebelung; Firas Khader; Jakob Nikolas Kather; Daniel Truhn (MICCAI (7), 2024)

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)

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)

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)

Autonomous Artificial Intelligence Agents for Clinical Decision Making in Oncology. - Dyke Ferber; Omar S. M. El Nahhas; Georg Wölflein; Isabella C. Wiest; Jan Clusmann; Marie-Elisabeth Leßman; Sebastian Foersch; Jacqueline Lammert; Maximilian Tschochohei; Dirk Jäger; Manuel Salto-Tellez; Nikolaus Schultz; et al. (CoRR, 2024)

Compute-Efficient Medical Image Classification with Softmax-Free Transformers and Sequence Normalization. - Firas Khader; Omar S. M. El Nahhas; Tianyu Han; Gustav Müller-Franzes; Sven Nebelung; Jakob Nikolas Kather; Daniel Truhn (CoRR, 2024)

End-To-End Clinical Trial Matching with Large Language Models. - Dyke Ferber; Lars Hilgers; Isabella C. Wiest; Marie-Elisabeth Leßmann; Jan Clusmann; Peter Neidlinger; Jiefu Zhu; Georg Wölflein; Jacqueline Lammert; Maximilian Tschochohei; Heiko Böhme; Dirk Jäger; et al. (CoRR, 2024)

RadioRAG: Factual Large Language Models for Enhanced Diagnostics in Radiology Using Dynamic Retrieval Augmented Generation. - Soroosh Tayebi Arasteh; Mahshad Lotfinia; Keno K. Bressem; Robert Siepmann; Dyke Ferber; Christiane Kuhl; Jakob Nikolas Kather; Sven Nebelung; Daniel Truhn (CoRR, 2024)

Prompt Injection Attacks on Large Language Models in Oncology. - Jan Clusmann; Dyke Ferber; Isabella C. Wiest; Carolin V. Schneider; Titus J. Brinker; Sebastian Foersch; Daniel Truhn; Jakob Nikolas Kather (CoRR, 2024)

Benchmarking foundation models as feature extractors for weakly-supervised computational pathology. - Peter Neidlinger; Omar S. M. El Nahhas; Hannah Sophie Muti; Tim Lenz; Michael Hoffmeister; Hermann Brenner; Marko van Treeck; Rupert Langer; Bastian Dislich; Hans-Michael Behrens; Christoph Röcken; Sebastian Foersch; et al. (CoRR, 2024)

Dermatologist-like explainable AI enhances melanoma diagnosis accuracy: eye-tracking study. - Tirtha Chanda; Sarah Haggenmüller; Tabea-Clara Bucher; Tim Holland-Letz; Harald Kittler; Philipp Tschandl; Markus V. Heppt; Carola Berking; Jochen S. Utikal; Bastian Schilling; Claudia Buerger; Cristian Navarrete-Dechent; et al. (CoRR, 2024)

Pathologist-like explainable AI for interpretable Gleason grading in prostate cancer. - Gesa Mittmann; Sara Laiouar-Pedari; Hendrik Alexander Mehrtens; Sarah Haggenmüller; Tabea-Clara Bucher; Tirtha Chanda; Nadine T. Gaisa; Mathias Wagner; Gilbert Georg Klamminger; Tilman T. Rau; Christina Neppl; Eva Maria Compérat; et al. (CoRR, 2024)

Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2. - Gustav Müller-Franzes; Firas Khader; Robert Siepmann; Tianyu Han; Jakob Nikolas Kather; Sven Nebelung; Daniel Truhn (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)

Navigating the European Union Artificial Intelligence Act for Healthcare. - Felix Busch; Jakob Nikolas Kather; Christian Johner; Marina Moser; Daniel Truhn; Lisa C. Adams; Keno K. Bressem (npj Digit. Medicine, 2024)

Augmented non-hallucinating large language models as medical information curators. - Stephen Gilbert; Jakob Nikolas Kather; Aidan Hogan (npj Digit. Medicine, 2024)

Medical large language models are susceptible to targeted misinformation attacks. - Tianyu Han; Sven Nebelung; Firas Khader; Tianci Wang; Gustav Müller-Franzes; Christiane Kuhl; Sebastian Försch; Jens Kleesiek; Christoph Haarburger; Keno K. Bressem; Jakob Nikolas Kather; Daniel Truhn (npj Digit. Medicine, 2024)

Privacy-preserving large language models for structured medical information retrieval. - Isabella C. Wiest; Dyke Ferber; Jiefu Zhu; Marko van Treeck; Sonja K. Meyer; Radhika Juglan; Zunamys I. Carrero; Daniel Paech; Jens Kleesiek; Matthias P. Ebert; Daniel Truhn; Jakob Nikolas Kather (npj Digit. Medicine, 2024)

Encrypted federated learning for secure decentralized collaboration in cancer image analysis. - Daniel Truhn; Soroosh Tayebi Arasteh; Oliver Lester Saldanha; Gustav Müller-Franzes; Firas Khader; Philip Quirke; Nicholas P. West; Richard Gray 005; Gordon G. A. Hutchins; Jacqueline A. James; Maurice B. Loughrey; Manuel Salto-Tellez; et al. (Medical Image Anal., 2024)

LLM Agents Making Agent Tools. - Georg Wölflein; Dyke Ferber; Daniel Truhn; Ognjen Arandjelovic; Jakob Nikolas Kather (ACL (1), 2025)

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

Diagnosis with Nanoscale Protein Distributions: Single-Molecule Fluorescence Localization Microscopy and Attention-Based Learning. - Alistair Curd; Hayley Slaney; Scarlet F. Brockmöller; Andras-Gabor Miklosi; Deep Arora; Andrew Lewington; James H. Felce; Omar S. M. El Nahhas; Marko van Treeck; Jakob Nikolas Kather; Phil Quirke (ISBI, 2025)

Benchmarking vision-language models for diagnostics in emergency and critical care settings. - Christoph F. Kurz; Tatiana Merzhevich; Bjoern M. Eskofier; Jakob Nikolas Kather; Benjamin Gmeiner (npj Digit. Medicine, 2025)

Large language models-enabled digital twins for precision medicine in rare gynecological tumors. - Jacqueline Lammert; Nicole Pfarr; Leonid Kuligin; Sonja Mathes; Tobias Dreyer; Luise Modersohn; Patrick Metzger; Dyke Ferber; Jakob Nikolas Kather; Daniel Truhn; Lisa Christine Adams; Keno K. Bressem; et al. (npj Digit. Medicine, 2025)

Examining human-AI interaction in real-world healthcare beyond the laboratory. - Magdalena Katharina Wekenborg; Stephen Gilbert; Jakob Nikolas Kather (npj Digit. Medicine, 2025)

How machine learning on real world clinical data improves adverse event recording for endoscopy. - Stefan Wittlinger; Isabella C. Wiest; Mahboubeh Jannesari Ladani; Jakob Nikolas Kather; Matthias P. Ebert; Fabian Siegel; Sebastian Belle (npj Digit. Medicine, 2025)

A deep learning framework for efficient pathology image analysis. - Peter Neidlinger; Tim Lenz; Sebastian Foersch; Chiara Maria Lavinia Loeffler; Jan Clusmann; Marco Gustav; Lawrence A. Shaktah; Rupert Langer; Bastian Dislich; Lisa A. Boardman; Amy J. French; Ellen L. Goode; et al. (CoRR, 2025)

Federated EndoViT: Pretraining Vision Transformers via Federated Learning on Endoscopic Image Collections. - Max Kirchner; Alexander C. Jenke; Sebastian Bodenstedt; Fiona R. Kolbinger; Oliver Lester Saldanha; Jakob Nikolas Kather; Martin Wagner 001; Stefanie Speidel (CoRR, 2025)

A European Multi-Center Breast Cancer MRI Dataset. - Gustav Müller-Franzes; Lorena Escudero Sanchez; Nicholas Payne; Alexandra Athanasiou; Michael Kalogeropoulos; Aitor Lopez; Alfredo Miguel Soro Busto; Julia Camps Herrero; Nika Rasoolzadeh; Tianyu Zhang; Ritse Mann; Debora Jutz; et al. (CoRR, 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)

LongHealth: A Question Answering Benchmark with Long Clinical Documents. - Lisa Adams; Felix Busch; Tianyu Han; Jean-Baptiste Excoffier; Matthieu Ortala; Alexander Löser; Hugo J. W. L. Aerts; Jakob Nikolas Kather; Daniel Truhn; Keno K. Bressem (J. Heal. Informatics Res., 2025)