Renfei Liu

Renfei Liu

Postdoc, Guest Researcher

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    1 - 5 out of 5Page size: 10
    1. 2023
    2. Published

      SE(3) Group Convolutional Neural Networks and a Study on Group Convolutions and Equivariance for DWI Segmentation

      Liu, Renfei, Lauze, Francois Bernard, Bekkers, E. J., Erleben, Kenny & Darkner, Sune, 2023, Research Square, 32 p.

      Research output: Working paper › Preprint › Research

    3. 2022
    4. Published

      Bundle geodesic convolutional neural network for diffusion-weighted imaging segmentation

      Liu, Renfei, Lauze, Francois Bernard, Erleben, Kenny, Berg, Rune W. & Darkner, Sune, 2022, In: Journal of Medical Imaging. 9, 6, 14 p., 064002.

      Research output: Contribution to journal › Journal article › Research › peer-review

    5. Published

      Group Convolutional Neural Networks for DWI Segmentation

      Liu, Renfei, Lauze, Francois Bernard, Bekkers, E. J. & Erleben, Kenny, 2022, In: Proceedings of Machine Learning Research. 2022, 1, p. 1-11

      Research output: Contribution to journal › Conference article › Research

    6. Published

      Group and Pseudo-group Convolutional Neural Networks

      Liu, Renfei, 2022, Department of Computer Science, Faculty of Science, University of Copenhagen. 91 p.

      Research output: Book/Report › Ph.D. thesis › Research

    7. 2021
    8. Published

      Bundle Geodesic Convolutional Neural Network for DWI Segmentation from Single Scan Learning

      Liu, Renfei, Lauze, Francois Bernard, Erleben, Kenny & Darkner, Sune, 2021, Computational Diffusion MRI - 12th International Workshop, CDMRI 2021, Held in Conjunction with MICCAI 2021, Proceedings. Cetin-Karayumak, S., Christiaens, D., Figini, M., Guevara, P., Gyori, N., Nath, V. & Pieciak, T. (eds.). Springer, p. 121-132 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 13006 LNCS).

      Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

    ID: 214001946