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@INPROCEEDINGS{Schiffer:1018410,
      author       = {Schiffer, Christian and Amunts, Katrin and Dickscheid,
                      Timo},
      title        = {{T}he status quo of automated cytoarchitecture analysis:
                      {W}here are we, and where are we going?},
      reportid     = {FZJ-2023-04791},
      year         = {2023},
      abstract     = {Cytoarchitectonic brain maps provide a microstructural
                      reference for multi-modal human brain atlases, representing
                      important indicators for brain connectivity and function.
                      Cytoarchitectonic areas are defined by characteristic
                      microstructural cell distributions, including the size,
                      shape, type, orientation, and density of neurons, as well as
                      their distinct laminar and columnar arrangement.
                      High-resolution microscopic scans of histological human
                      brain sections enable identifying cytoarchitectonic brain
                      areas. Modern high-throughput microscopic scanners enable
                      large-scale image acquisition, resulting in petabyte-scale
                      microscopic imaging datasets that provide the foundation for
                      next-generation brain atlases. As established
                      cytoarchitectonic brain mapping methods based on statistical
                      image analysis do not scale to such large datasets, ongoing
                      research aims to develop methods for automatic
                      classification and characterization of cytoarchitecture
                      based on large amounts of high-resolution images.In this
                      presentation, we will give an overview of the current state
                      of automated cytoarchitecture analysis and provide an
                      outlook on future developments in the field. We will discuss
                      the roles, potentials, and challenges of supervised
                      learning, self-supervised representation learning, and
                      graph-based inference at whole-brain level in the context of
                      cytoarchitecture analysis. Finally, we will comment on the
                      potential impact of novel methods and technologies on the
                      field, including zero-shot learning, data-driven
                      cytoarchitectonic mapping, multi-modal latent space fusion,
                      and exascale computing.},
      month         = {Oct},
      date          = {2023-10-04},
      organization  = {7th BigBrain Workshop, Reykjavík
                       (Iceland), 4 Oct 2023 - 6 Oct 2023},
      subtyp        = {After Call},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / 5254 - Neuroscientific Data Analytics and AI
                      (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)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)POF4-5254 /
                      G:(DE-HGF)InterLabs-0015 / G:(DE-Juel-1)E.40401.62},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1018410},
}