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@ARTICLE{Schiffer:888547,
      author       = {Schiffer, Christian and Amunts, Katrin and Harmeling,
                      Stefan and Dickscheid, Timo},
      title        = {{C}ontrastive {R}epresentation {L}earning for {W}hole
                      {B}rain {C}ytoarchitectonic {M}apping in {H}istological
                      {H}uman {B}rain {S}ections},
      reportid     = {FZJ-2020-05011},
      year         = {2020},
      note         = {Preprint submitted to ISBI 2021},
      abstract     = {Cytoarchitectonic maps provide microstructural reference
                      parcellations of the brain, describing its organization in
                      terms of the spatial arrangement of neuronal cell bodies as
                      measured from histological tissue sections. Recent work
                      provided the first automatic segmentations of
                      cytoarchitectonic areas in the visual system using
                      Convolutional Neural Networks. We aim to extend this
                      approach to become applicable to a wider range of brain
                      areas, envisioning a solution for mapping the complete human
                      brain. Inspired by recent success in image classification,
                      we propose a contrastive learning objective for encoding
                      microscopic image patches into robust microstructural
                      features, which are efficient for cytoarchitectonic area
                      classification. We show that a model pre-trained using this
                      learning task outperforms a model trained from scratch, as
                      well as a model pre-trained on a recently proposed auxiliary
                      task. We perform cluster analysis in the feature space to
                      show that the learned representations form anatomically
                      meaningful groups.},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) / HBP
                      SGA2 - Human Brain Project Specific Grant Agreement 2
                      (785907) / HBP SGA3 - Human Brain Project Specific Grant
                      Agreement 3 (945539) / Helmholtz AI - Helmholtz Artificial
                      Intelligence Coordination Unit – Local Unit FZJ
                      (E.40401.62)},
      pid          = {G:(DE-HGF)POF3-574 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539 / G:(DE-Juel-1)E.40401.62},
      typ          = {PUB:(DE-HGF)25},
      eprint       = {2011.12865},
      howpublished = {arXiv:2011.12865},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2011.12865;\%\%$},
      url          = {https://juser.fz-juelich.de/record/888547},
}