Hauptseite > Publikationsdatenbank > Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections > print

001     888547
005     20231123201912.0
024 7 _ |a arXiv:2011.12865
|2 arXiv
024 7 _ |a 2128/26381
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024 7 _ |a altmetric:94939385
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037 _ _ |a FZJ-2020-05011
100 1 _ |a Schiffer, Christian
|0 P:(DE-Juel1)170068
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245 _ _ |a Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections
260 _ _ |c 2020
336 7 _ |a Preprint
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336 7 _ |a ARTICLE
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500 _ _ |a Preprint submitted to ISBI 2021
520 _ _ |a 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.
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536 _ _ |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)
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|x 1
536 _ _ |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)
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536 _ _ |a Helmholtz AI - Helmholtz Artificial Intelligence Coordination Unit – Local Unit FZJ (E.40401.62)
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700 1 _ |a Amunts, Katrin
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700 1 _ |a Harmeling, Stefan
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700 1 _ |a Dickscheid, Timo
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856 4 _ |u https://juser.fz-juelich.de/record/888547/files/Schiffer_etal_biorXiv_ISBI_2020_preprint.pdf
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914 1 _ |y 2020
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