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@INPROCEEDINGS{Schiffer:1048781,
      author       = {Schiffer, Christian and Spitzer, Hannah and Kropp,
                      Jan-Oliver and Thönnissen, Andre and Berr, Katja and
                      Amunts, Katrin and Boztoprak, Zeynep and Dickscheid, Timo},
      title        = {{C}yto{N}et: {A} {F}oundation {M}odel for the {H}uman
                      {C}erebral {C}ortex - {A}pplications in {B}ig{B}rain and
                      {B}eyond},
      reportid     = {FZJ-2025-04896},
      year         = {2025},
      abstract     = {CytoNet: A Foundation Model for the Human Cerebral Cortex -
                      Applications in BigBrain and Beyond 28 Oct 2025, 12:00 15m
                      Langenbeck-Virchow-HausTalk Session 1: Multiscale Data
                      Integration $\&$ AI-based ProcessingSpeaker Christian
                      Schiffer (Forschungszentrum Jülich)DescriptionMicroscopic
                      analysis of cytoarchitecture in the human cerebral cortex is
                      essential for understanding the anatomical basis of brain
                      function. We present CytoNet, a foundation model that
                      encodes high-resolution microscopic image patches into
                      expressive feature representations suitable for whole-brain
                      analysis. CytoNet leverages the spatial relationship between
                      anatomical proximity and microstructural similarity to learn
                      biologically meaningful features using self-supervised
                      learning, without the need for manual annotations. The
                      learned features are consistent across regions and subjects,
                      can be computed at arbitrarily dense sampling locations, and
                      support a wide range of neuroscientific analyses. We
                      demonstrate state-of-the-art performance for brain area
                      classification, cortical layer segmentation, morphological
                      parameter estimation, and unsupervised parcellation. As a
                      foundation model, CytoNet provides a unified representation
                      of cortical microarchitecture and establishes a basis for
                      comprehensive analyses of cytoarchitecture and its
                      relationship to other structural and functional principles
                      at the whole-brain level.},
      month         = {Oct},
      date          = {2025-10-27},
      organization  = {9th BigBrain Workshop - HIBALL Closing
                       Symposium, Berlin (Germany), 27 Oct
                       2025 - 29 Oct 2025},
      subtyp        = {After Call},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / HIBALL - Helmholtz International BigBrain
                      Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)
                      / Helmholtz AI - Helmholtz Artificial Intelligence
                      Coordination Unit – Local Unit FZJ (E.40401.62) / X-BRAIN
                      (ZT-I-PF-4-061) / JL SMHB - Joint Lab Supercomputing and
                      Modeling for the Human Brain (JL SMHB-2021-2027)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)POF4-5251 /
                      G:(DE-HGF)InterLabs-0015 / G:(DE-Juel-1)E.40401.62 /
                      G:(DE-HGF)ZT-I-PF-4-061 / G:(DE-Juel1)JL SMHB-2021-2027},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1048781},
}