Kather, Jakob Nikolas

Benchmarking artificial intelligence methods for end-to-end computational pathology

Laleh, N. G., Muti, H. S., Lavinia Loeffler, C. M., Echle, A., Saldanha, O. L., Mahmood, F., Lu, M. Y., Trautwein, C., Langer, R., Dislich, B., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory)

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 13

Direct prediction of Homologous Recombination Deficiency from routine histology in ten different tumor types with attention-based Multiple Instance Learning: a development and validation study

Lavinia Loeffler, C. M., El Nahhas, O. S. M., Muti, H. S., Seibel, T., Cifci, D., Treeck, M. V., Gustav, M., Carrero, Z. I., Gaisa, N. T., Lehmann, K.-V., et al. (2023) · medRxiv

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 12

End-To-End Clinical Trial Matching with Large Language Models

Ferber, D., Hilgers, L., Wiest, I. C., Leßmann, M.-E., Clusmann, J., Neidlinger, P., Zhu, J., Wölflein, G., Lammert, J., Tschochohei, M., et al. (2024) · arXiv

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 12

A 3-Dimensional Histology Computer Model of Malignant Melanoma and Its Implications for Digital Pathology

Kurz, A., Krahl, D., Kutzner, H., Barnhill, R., Perasole, A., Figueras, M. T. F., Ferrara, G., Braun, S. A., Starz, H., Llamas-Velasco, M., et al. (2023) · European Journal of Cancer

AI in cancer detectionCutaneous Melanoma Detection and Management

Cited by 10

LongHealth: A Question Answering Benchmark with Long Clinical Documents

Adams, L., Busch, F., Han, T., Excoffier, J.-B., Ortala, M., Löser, A., Aerts, H. J., Kather, J. N., Truhn, D., & Bressem, K. (2024) · arXiv

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 10

LLM Agents Making Agent Tools

Wölflein, G., Ferber, D., Truhn, D., Arandjelović, O., & Kather, J. N. (2025) · arXiv

Software Engineering ResearchScientific Computing and Data Management

Cited by 3

Abstract P2-06-18: Deep learning-based tumor microenvironment signature predicts progression-free survival in early-stage hormone receptor positive and HER2 negative breast cancer

Khader, F., El Nahhas, O. S. M., Khader, F., Paul, E., Cekan, P., Antone, N., Truhn, D., & Kather, J. N. (2025) · Clinical Cancer Research

AI in cancer detectionBreast Cancer Treatment Studies

Cited by 0