Dominik Drees
Research Associate
dominik.drees(AT)uni-muenster.de
Research Foci
- Volume and image processing
- Ressource constrained (esp. out-of-core) computing
- Nanophotonic neural networks
CV
Academic Education
- Ph. D. studies, Pattern Recognition and Image Analysis group (Prof. Xiaoyi Jiang)
- Master of Science (Computer Science), University of Münster
Honors
- Förderpreis der Angewandten Informatik (1st prize) – IHK Nord Westfalen
- SciVis Contest Award – IEEE VIS: Visualization & Visual Analytics
External Function
- University of Münster (Voreen-Project, Core Team Member)
Publications
- Becker, Marlon, Drees, Dominik, Brückerhoff-Plückelmann, Frank, Schuck, Carsten, Pernice, Wolfram, and Risse, Benjamin. . “Activation Functions in Non-Negative Neural Networks.” contributed to the Machine Learning and the Physical Sciences Workshop, NeurIPS, New Orleans
- Drees, D, Eilers, F, and Jiang, X. . “Hierarchical random walker segmentation for large volumetric biomedical images.” IEEE Transactions on Image Processing, № 31: 4431–4446. doi: 10.1109/TIP.2022.3185551.
- Drees, D, Eilers, F, Bian, A, and Jiang, X. . “A Bhattacharyya coefficient-based framework for noise model-aware random walker image segmentation.” in Proc. of GCPR, edited by B Andres, F Bernard, D Cremers, S Frintrop, B Goldlücke and I Ihrke. Berlin: Springer Nature.
- Drees, Dominik. . “Efficient Out-of-Core Methods for Biomedical Volume Processing and Analysis.” Dissertation thesis, University of Münster.
- Drees, D, Scherzinger, A, Hägerling, R, Kiefer, F, and Jiang, X. . “Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets.” BMC Bioinformatics, № 22 (1) 346. doi: 10.1186/s12859-021-04262-w.
- Kirschnick, Nils, Drees, Dominik, Redder, Esther, Erapaneedi, Raghu, Pereira da Graca, Abel, Schäfers, Michael, Jiang, Xiaoyi, and Kiefer, Friedemann. . “Rapid methods for the evaluation of fluorescent reporters in tissue clearing and the segmentation of large vascular structures.” iScience, № 24 (6) 102650. doi: 10.1016/j.isci.2021.102650.
- Drees, D, Scherzinger, A, and Jiang, X. . “GERoMe – a method for evaluating stability of graph extraction algorithms without ground truth.” IEEE Access, № 7: 21744–21755. doi: 10.1109/ACCESS.2019.2898754.
- Klemm, S, Scherzinger, A, Drees, D, and Jiang, X. . Barista - a graphical tool for designing and training deep neural networks, arXiv e-print:1802.04626: CoRR.
- Hägerling, R, Drees, D, Scherzinger, A, Dierkes, C, Martin-Almedina, S, Butz, S, Gordon, K, Schäfers, M, Hinrichs, K, Ostergaard, P, Vestweber, D, Goerge, T, Mansour, S, Jiang, X, Mortimer, P, and Kiefer, F. . “VIPAR, a quantitative approach to 3D-histopathology applied to lymphatic malformations.” JCI Insight, № 2 (16): e93424.
- Scherzinger, Aaron, Brix, Tobias, Drees, Dominik, Völker, Andreas, Radkov, Kiril, Santalidis, Niko, Fieguth, Alexander, and Hinrichs, Klaus H. . “Interactive Exploration of Cosmological Dark-Matter Simulation Data.” IEEE Computer Graphics and Applications, № 37 (2): 80–89.
- Drees, D, Scherzinger, A, and Jiang, X. . “GERoMe - A novel graph extraction robustness measure.” contribution to the Proc. of Int. Workshop on Graph-Based Representations in Pattern Recognition (GbR), Anacapri, Italy
- Scherzinger, Aaron, Brix, Tobias, Drees, Dominik, Völker, Andreas, Radkov, Kiril, Santalidis, Niko, Fieguth, Alexander, and Hinrichs, Klaus H. . “Visualize the Universe: Interactive Exploration of Cosmological Dark Matter Simulation Data.” contribution to the IEEE Visualization Conference 2015 October 25-30, Chicago, Il, USA New York City: Wiley-IEEE Press.