Publikationen von Nico Scherf

Podranski, K.; Zhang, R.; Thierbach, K.; Thieleking, R.; Witte, A. V.; Villringer, A.; Weiskopf, N.; Scherf, N.: Detecting fat artifacts in diffusion MRI with deep learning. 9th IMPRS NeuroCom Summer School, Leipzig, Germany (2019)

Podranski, K.; Scherf, N.; Weiskopf, N.: Improving high-resolution qunatitative MRI maps for in-vivo histology MRI of the human brain. Medical Imaging Summer School 2018 - Medical Imaging Meets Deep Learning, Favignana, Italy (2018)

Scherf, N.: A gentle introduction to deep learning. Vorlesung: 11th IMPRS NeuroCom Summer School, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 27. Juni 2022 - 29. Juni 2022

Schäfer, T. A. J.; Nitsch, A.; Scherf, N.: fMRI preprocessing workshop. Vorlesung: Max Planck School of Cognition, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 01. April 2022 - 30. September 2022

Brammerloh, M.; Mueller, K.; Schmidt, J.; Lorenz, R.; Lipp, I.; Chaimow, D.; Eichner, C.; Kirilina, E.; Scherf, N.: Neuroimaging physics and signal processing: Basic MR acquisition & data analysis. Vorlesung: IMPRS NeuroCom Lecture Series, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 04. Oktober 2021 - 07. Oktober 2021

Scherf, N.; Mueller, K.: Data Science Clinic. Vorlesung: Seminar, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 01. Januar 2021 - 31. Dezember 2021

Mueller, K.; Scherf, N.: Methods Club. Vorlesung: Seminar, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 01. Januar 2020 - 31. Dezember 2020

Hinnerichs, T.: Finding gene expression patterns and their link to connectivities in mouse brains by exploiting interaction networks. Diplom, TU Dresden, Germany (2022)

Kiakou, D.: Geometric deep learning in neuroimaging. Master, Hellenic Open University, Patras, Greece (2022)

Meussling, M.: Segmentierung und Analyse von Astrozyten und Nervenfasern des menschlichen Gehirns. Master, Hochschule für Technik, Wirtschaft und Kultur, Leipzig, Germany (2022)

Mueller, G.: Quantifying differences in the microanatomy of the auditory cortex between both hemispheres of the mammalian brain. Master, TU Dresden, Germany (2022)

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

Kloenne, M.; Niehaus, S.; Lampe, L.; Merola, A.; Reinelt, J.; Scherf, N.: A framework for CT image segmentation inspired by the clinical environment. arXiv (2019)

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)

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)