Horlava, N.: Robust, structure-aware classification of neurodegeneration. Master, TU Dresden, Germany (2021)

Mueller, K.; Růžička, F.; Slovák, M.; Forejtová, Z.; Dušek, P.; Dušek, P.; Jech, R.; Serranová, T.: Centrality and seed-based correlation maps obtained with functional MRI. (2022)

Dabbagh, A.; Horn, U.; Kaptan, M.; Mildner, T.; Müller, R.; Lepsien, J.; Weiskopf, N.; Brooks, J. C.W.; Finsterbusch, J.; Eippert, F.: Reliability of task-based fMRI in the dorsal horn of the human spinal cord. (2023)

Williams, E.; Kienast, M.; Medawar, E.; Reinelt, J.; Merola, A.; Klopfenstein, S. A. I.; Flint, A. R.; Heeren, P.; Poncette, A.-S.; Balzer, F. et al.; Beimes, J.; von Bünau, P.; Chromik, J.; Arnrich, B.; Scherf, N.; Niehaus, S.: FHIR-DHP: A standardized clinical data harmonisation pipeline for scalable AI application deployment. (2022)

Paul, K. I.; Mueller, K.; Rousseau, P.-N.; Glathe, A.; Taatgen, N. A.; Cnossen, F.; Lanzer, P.; Villringer, A.; Steele, C.: Visuo-motor transformations in the intraparietal sulcus mediate the acquisition of endovascular medical skill. (2022)

Waschke, J.; Hlawitschka, M.; Anlas, K.; Trivedi, V.; Roeder, I.; Huisken, J.; Scherf, N.: linus: Conveniently explore, share, and present large-scale biological trajectory data from a web browser. (2021)

Hoffmann, H.; Baldow, C.; Zerjatke, T.; Gottschalk, A.; Wagner, S.; Karg, E.; Niehaus, S.; Roeder, I.; Glauche, I.; Scherf, N.: How to predict relapse in leukaemia using time series data: A comparative in silico study. (2020)

Horlava, N.; Mironenko, A.; Niehaus, S.; Wagner, S.; Roeder, I.; Scherf, N.: A comparative study of semi- and self-supervised semantic segmentation of biomedical microscopy data. (2020)

Wagner, S.; Thierbach, K.; Zerjatke, T.; Glauche, I.; Roeder, I.; Scherf, N.: TraCurate: Efficiently curating cell tracks. (2020)

Scherf, N.: Computer statt Mensch: Kann KI unsere Probleme lösen? (2022)