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Guarnier, G.; Reinelt, J.; Molloy, E. N.; Mihai, P. G.; Einaliyan, P.; Valk, S. L.; Modestino, A.; Ugolini, M.; Mueller, K.; Wu, Q. et al.; Babayan, A.; Castellaro, M.; Villringer, A.; Scherf, N.; Thierbach, K.; Schroeter, M. L.; Alzheimers Disease Neuroimaging Initiative; Australian Imaging Biomarkers and Lifestyle flagship study of ageing; Frontotemporal Lobar Degeneration Neuroimaging Initiative: Cascaded multimodal deep learning in the differential diagnosis, progression prediction, and staging of Alzheimer's and frontotemporal dementia. medRxiv (2024)

Hofmann, S.; Goltermann, O.; Scherf, N.; Müller, K.-R.; Löffler, M.; Villringer, A.; Gaebler, M.; Witte, A. V.; Beyer, F.: The utility of explainable A.I. for MRI analysis: Relating model predictions to neuroimaging features of the aging brain. bioRxiv (2024)

Hofmann, S.; Ciston, A.; Koushik, A.; Klotzsche, F.; Hebart, M. N.; Müller, K.-R.; Villringer, A.; Scherf, N.; Hilsmann, A.; Nikulin, V. V. et al.; Gaebler, M.: Human-aligned deep and sparse encoding models of dynamic 3D face similarity perception. PsyArXiv (2024)

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. bioRxiv (2024)

Titone, L.; Milosevic, N.; Meyer, L.: The ARC toolbox: Artificial languages with rhythmicity control. bioRxiv (2024)

Haase, R.; Tischer, C.; Scherf, N.: Benchmarking large language models for bio-image analysis code generation. bioRxiv (2024)

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. medRxiv (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. bioRxiv (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. medRxiv (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. arXiv (2020)

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

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