Dominik Drees

Research Associate
© Uni MS

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; 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, 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, Jiang X. . ‘A Bhattacharyya coefficient-based framework for noise model-aware random walker image segmentation.’ In Proc. of GCPR, edited by Andres B, Bernard F, Cremers D, Frintrop S, Goldlücke B, Ihrke I, 166–181. Berlin: Springer Nature.
    • Drees, Dominik. . Efficient Out-of-Core Methods for Biomedical Volume Processing and Analysis Dissertation thesis, University of Münster. ULB Münster.

    • Drees D, Scherzinger A, Hägerling R, Kiefer F, 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 ; 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, 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, 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, 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, Hinrichs Klaus H. . ‘Interactive Exploration of Cosmological Dark-Matter Simulation Data.’ IEEE Computer Graphics and Applications 37, № 2: 80–89.
    • Drees D, Scherzinger A, Jiang X. . ‘GERoMe - A novel graph extraction robustness measure.’ Contributed 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, Hinrichs Klaus H. . ‘Visualize the Universe: Interactive Exploration of Cosmological Dark Matter Simulation Data.’ Contributed to the IEEE Visualization Conference 2015 October 25-30, Chicago, Il, USA.