Publications of Nico Scherf

Podranski, K., Zhang, R., Thierbach, K., Thieleking, R., Witte, A. V., Villringer, A., … Scherf, N. Detecting fat artifacts in diffusion MRI with deep learning. Poster presented at the 9th IMPRS NeuroCom Summer School, Leipzig, Germany. Retrieved from http://hdl.handle.net/21.11116/0000-0004-C6C1-2

Podranski, K., Scherf, N., & Weiskopf, N. Improving high-resolution qunatitative MRI maps for in-vivo histology MRI of the human brain. Poster presented at the Medical Imaging Summer School 2018 - Medical Imaging Meets Deep Learning, Favignana, Italy. Retrieved from http://hdl.handle.net/21.11116/0000-0004-C6C8-B

Scherf, N. A gentle introduction to deep learning. Courseware_lecture presented at the 11th IMPRS NeuroCom Summer School, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. Retrieved from http://hdl.handle.net/21.11116/0000-000B-1A85-2

Schäfer, T. A. J., Nitsch, A., & Scherf, N. fMRI preprocessing workshop. Courseware_lecture presented at the Max Planck School of Cognition, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. Retrieved from http://hdl.handle.net/21.11116/0000-000B-3694-1

Brammerloh, M., Mueller, K., Schmidt, J., Lorenz, R., Lipp, I., Chaimow, D., … Scherf, N. Neuroimaging physics and signal processing: Basic MR acquisition & data analysis. Courseware_lecture presented at the IMPRS NeuroCom Lecture Series, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. Retrieved from http://hdl.handle.net/21.11116/0000-000A-EF24-1

Scherf, N., & Mueller, K. Data Science Clinic. Courseware_lecture presented at the Seminar, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. Retrieved from http://hdl.handle.net/21.11116/0000-000B-2D55-4

Mueller, K., & Scherf, N. Methods Club. Courseware_lecture presented at the Seminar, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. Retrieved from http://hdl.handle.net/21.11116/0000-000B-2D59-0

Hinnerichs, T. (2022). Finding gene expression patterns and their link to connectivities in mouse brains by exploiting interaction networks (Diploma Thesis). TU Dresden, Germany. Retrieved from http://hdl.handle.net/21.11116/0000-000B-2F42-7

Kiakou, D. (2022). Geometric deep learning in neuroimaging (Master's Thesis). Hellenic Open University, Patras, Greece. Retrieved from http://hdl.handle.net/21.11116/0000-000B-2F30-B

Meussling, M. (2022). Segmentierung und Analyse von Astrozyten und Nervenfasern des menschlichen Gehirns (Master's Thesis). Hochschule für Technik, Wirtschaft und Kultur, Leipzig, Germany. Retrieved from http://hdl.handle.net/21.11116/0000-000B-2F34-7

Mueller, G. (2022). Quantifying differences in the microanatomy of the auditory cortex between both hemispheres of the mammalian brain (Master's Thesis). TU Dresden, Germany. Retrieved from http://hdl.handle.net/21.11116/0000-000B-2F39-2

Horlava, N. (2021). Robust, structure-aware classification of neurodegeneration (Master's Thesis). TU Dresden, Germany. Retrieved from http://hdl.handle.net/21.11116/0000-000B-2F37-4

Kloenne, M., Niehaus, S., Lampe, L., Merola, A., Reinelt, J., & Scherf, N. (2019, September 27). A framework for CT image segmentation inspired by the clinical environment. arXiv. Retrieved from http://hdl.handle.net/21.11116/0000-0004-C770-D

Guarnier, G., Reinelt, J., Molloy, E. N., Mihai, P. G., Einaliyan, P., Valk, S. L., … Frontotemporal Lobar Degeneration Neuroimaging Initiative. (2024, October 17). Cascaded multimodal deep learning in the differential diagnosis, progression prediction, and staging of Alzheimer's and frontotemporal dementia. MedRxiv. doi:10.1101/2024.09.23.24314186

Hofmann, S., Goltermann, O., Scherf, N., Müller, K.-R., Löffler, M., Villringer, A., … Beyer, F. (2024, October 1). The utility of explainable A.I. for MRI analysis: Relating model predictions to neuroimaging features of the aging brain. BioRxiv. doi:10.1101/2024.09.27.615357

Hofmann, S., Ciston, A., Koushik, A., Klotzsche, F., Hebart, M. N., Müller, K.-R., … Gaebler, M. (2024, September 16). Human-aligned deep and sparse encoding models of dynamic 3D face similarity perception. PsyArXiv. doi:10.31234/osf.io/f62pw

Haase, R., Tischer, C., & Scherf, N. (2024, April 25). Benchmarking large language models for bio-image analysis code generation. BioRxiv. doi:10.1101/2024.04.19.590278

Williams, E., Kienast, M., Medawar, E., Reinelt, J., Merola, A., Klopfenstein, S. A. I., … Niehaus, S. (2022, November 13). FHIR-DHP: A standardized clinical data harmonisation pipeline for scalable AI application deployment. MedRxiv. doi:10.1101/2022.11.07.22281564

Waschke, J., Hlawitschka, M., Anlas, K., Trivedi, V., Roeder, I., Huisken, J., & Scherf, N. (2021, January 8). linus: Conveniently explore, share, and present large-scale biological trajectory data from a web browser. BioRxiv. doi:10.1101/2020.04.17.043323

Hoffmann, H., Baldow, C., Zerjatke, T., Gottschalk, A., Wagner, S., Karg, E., … Scherf, N. (2020, December 7). How to predict relapse in leukaemia using time series data: A comparative in silico study. MedRxiv. doi:10.1101/2020.12.04.20243907