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| 001 | 893921 | ||
| 005 | 20231123201912.0 | ||
| 024 | 7 | _ | |a arXiv:2011.12865 |2 arXiv |
| 024 | 7 | _ | |a altmetric:94939385 |2 altmetric |
| 037 | _ | _ | |a FZJ-2021-02931 |
| 100 | 1 | _ | |a Schiffer, Christian |0 P:(DE-Juel1)170068 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a 18th International Symposium on Biomedical Imaging (ISBI) |c Nice |d 2021-04-13 - 2021-04-16 |w France |
| 245 | _ | _ | |a Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections |
| 260 | _ | _ | |c 2021 |
| 295 | 1 | 0 | |a IEEE |
| 300 | _ | _ | |a 4 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Paper |2 DataCite |
| 336 | 7 | _ | |a Contribution to a conference proceedings |b contrib |m contrib |0 PUB:(DE-HGF)8 |s 1700723589_29181 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Contribution to a book |0 PUB:(DE-HGF)7 |2 PUB:(DE-HGF) |m contb |
| 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. |
| 536 | _ | _ | |a 5254 - Neuroscientific Data Analytics and AI (POF4-525) |0 G:(DE-HGF)POF4-5254 |c POF4-525 |f POF IV |x 0 |
| 536 | _ | _ | |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) |0 G:(EU-Grant)785907 |c 785907 |f H2020-SGA-FETFLAG-HBP-2017 |x 1 |
| 536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 2 |
| 536 | _ | _ | |a Helmholtz AI - Helmholtz Artificial Intelligence Coordination Unit – Local Unit FZJ (E.40401.62) |0 G:(DE-Juel-1)E.40401.62 |c E.40401.62 |x 3 |
| 700 | 1 | _ | |a Amunts, Katrin |0 P:(DE-Juel1)131631 |b 1 |
| 700 | 1 | _ | |a Harmeling, Stefan |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Dickscheid, Timo |0 P:(DE-Juel1)165746 |b 3 |
| 787 | 0 | _ | |a Schiffer, Christian et.al. |0 FZJ-2020-05011 |t Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections |i IsParent |d 2020 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/893921/files/Schiffer_etal_IEEE_2021.pdf |y Restricted |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/893921/files/Schiffer_etal_biorXiv_ISBI_2020_preprint.pdf |y Restricted |
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| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)170068 |
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| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)165746 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-525 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Decoding Brain Organization and Dysfunction |9 G:(DE-HGF)POF4-5254 |x 0 |
| 914 | 1 | _ | |y 2021 |
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