• Wessels, F., Kuntz S., Krieghoff-Henning E., Schmitt M., Braun V., Worst T.S., Neuberger M., Steeg M., Gaiser T., Fröhling S., Michel M.S., Nuhn P. and Brinker T.J. (2022). Artificial Intelligence to Predict Oncological Outcome Directly from Haematoxylin and Eosin-Stained Slides in Urology, Minerva Urology and Nephrology.
• Schneider L.*, Laiour-Pedari S.*, Kuntz S., Krieghoff-Henning E., Hekler A., Kather J.N., Gaiser T., Fröhling S. and Brinker T.J. (2022). Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review, European Journal of Cancer.
• Kiehl L.*, Kuntz S.*, Höhn J., Jutzi T., Krieghoff-Henning E., Kather J.N., Holland-Letz T., Kopp-Schneider A., Chang-Claude J., Brobeil A., von Kalle C., Fröhling S., Alwers E., Brenner H., Hoffmeister M. and Brinker T.J (2021). Deep learning can predict lymph node status directly from histology in colorectal cancer, European Journal of Cancer.
• Kuntz S., Krieghoff-Henning E., Kather J.N., Jutzi T., Höhn J., Kiehl L., Hekler A., Alwers E., von Kalle C., Fröhling S., Utikal J.S., Brenner H., Hoffmeister M. and Brinker T.J. (2021) Gastrointestinal Cancer Classification and Prognostics from Histology Using Deep Learning: Systematic Review, European Journal of Cancer.
• Kuntz S., Poeck B. and Strauss R. (2017). Visual Working Memory Requires Permissive and Instructive NO/cGMP Signalling at Presynapses in the Drosophila Central Brain. Current Biology.

Further publications: ORCID complete list of publications