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@ARTICLE{Zhao:283020,
      author       = {Zhao, Wenzhao and Albert, Steffen and Wichtmann, Barbara D.
                      and Schmitt, Angelika and Attenberger, Ulrike and Zöllner,
                      Frank G. and Hesser, Jürgen},
      title        = {{E}fficient 3{D} affinely equivariant {CNN}s with adaptive
                      fusion of augmented spherical {F}ourier–{B}essel bases},
      journal      = {Information fusion},
      volume       = {127},
      issn         = {1566-2535},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DZNE-2025-01432},
      pages        = {103823},
      year         = {2026},
      note         = {The code is available at
                      https://github.com/ZhaoWenzhao/WMCSFB.},
      abstract     = {Filter-decomposition-based group equivariant convolutional
                      neural networks (CNNs) have shown promising stability and
                      data efficiency for 3D image feature extraction. However,
                      these networks, which rely on parameter sharing and discrete
                      transformation groups, often underperform in modern deep
                      neural network architectures for processing volumetric
                      images with dense 3D textures, such as the common 3D medical
                      images. To address these limitations, this paper presents an
                      efficient non-parameter-sharing continuous 3D affine group
                      equivariant neural network for volumetric images. This
                      network uses an adaptive aggregation of Monte Carlo
                      augmented spherical Fourier–Bessel filter bases to improve
                      the efficiency and flexibility of 3D group equivariant CNNs
                      for volumetric data. Unlike existing methods that focus only
                      on angular orthogonality in filter bases, the introduced
                      spherical Bessel Fourier filter base incorporates both
                      angular and radial orthogonality to improve feature
                      extraction. Experiments on four medical image segmentation
                      datasets and two seismic datasets show that the proposed
                      methods achieve better affine group equivariance and
                      superior segmentation accuracy than existing 3D group
                      equivariant convolutional neural network layers,
                      significantly improving the training stability and data
                      efficiency of conventional CNN layers (at 0.05 significance
                      level).},
      cin          = {AG Radbruch},
      ddc          = {620},
      cid          = {I:(DE-2719)5000075},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1016/j.inffus.2025.103823},
      url          = {https://pub.dzne.de/record/283020},
}