• 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 Hematoxylin 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 prognostication 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 Signaling at Presynapses in the Drosophila Central Brain. Current Biology.

Weitere Publikationen: ORCID-Gesamt-Publikations-Verzeichnis