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@INPROCEEDINGS{Li:278685,
      author       = {Li, Tong and Liu, Anran and Kügler, David and Reuter,
                      Martin},
      editor       = {Colliot, Olivier and Mitra, Jhimli},
      title        = {{B}oost the adversarial learning with {F}ourier regulator:
                      bias-field correction on {MRI}},
      publisher    = {SPIE},
      reportid     = {DZNE-2025-00616},
      pages        = {134060Y},
      year         = {2025},
      comment      = {Medical Imaging 2025: Image Processing : [Proceedings] -
                      SPIE, 2025. - ISBN 97815106859019781510685901 -
                      doi:10.1117/12.3047299},
      booktitle     = {Medical Imaging 2025: Image Processing
                       : [Proceedings] - SPIE, 2025. - ISBN
                       97815106859019781510685901 -
                       doi:10.1117/12.3047299},
      abstract     = {In magnetic resonance imaging, signal intensity
                      inhomogeneities due to intrinsic bias field pose a
                      significant challenge for automated medical image analysis.
                      Conventional methods to mitigate these effects, such as
                      N4ITK, are time-consuming and unstable. The exploration of
                      deep learning alternative approaches is still at an unknown
                      stage. Previous studies have obtained preliminary results in
                      GAN-based models, but we found the difficulty in aligning
                      bias-corrected image domains with clean image domains during
                      adversarial learning may affect the retention of normal
                      organizational structures. Therefore, we propose a novel
                      Fourier regulator structure that can be integrated into the
                      general adversarial learning framework. It explicitly
                      decouples different levels of semantic features based on the
                      Fourier field and utilizes explicit feature learning to
                      enhance intrinsic coherence and promote more organized
                      domain alignment. By separating amplitude and phase features
                      as well as splitting low and high-frequency information, our
                      model preserves organizational details more efficiently and
                      explicitly separates intensities across organizational
                      boundaries. During the training process of adversarial
                      learning, the generator generates the target domain while
                      the regulator and discriminator are fixed; the regulator and
                      discriminator are updated in parallel while the generator is
                      fixed. Such a learning approach extends the original min-max
                      optimization problem of adversarial learning to a
                      multi-player mix-max optimization problem. The discriminator
                      can quickly draw the generative domain closer to the target
                      domain, while the regulator aligns the distance to the
                      target domain in a more explicit feature-learning manner.
                      Evaluated on the OASIS and BrainWeb datasets, our model
                      outperforms traditional and deep learning methods to enhance
                      homogeneity. It also shows consistent performance in other
                      image reconstruction tasks, demonstrating its generalization
                      capabilities.},
      month         = {Feb},
      date          = {2025-02-16},
      organization  = {SPIE Medical Imaging 2025: Image
                       Processing, San Diego (United States),
                       16 Feb 2025 - 21 Feb 2025},
      cin          = {AG Reuter},
      cid          = {I:(DE-2719)1040310},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1117/12.3047299},
      url          = {https://pub.dzne.de/record/278685},
}