000888547 001__ 888547
000888547 005__ 20231123201912.0
000888547 0247_ $$2arXiv$$aarXiv:2011.12865
000888547 0247_ $$2Handle$$a2128/26381
000888547 0247_ $$2altmetric$$aaltmetric:94939385
000888547 037__ $$aFZJ-2020-05011
000888547 1001_ $$0P:(DE-Juel1)170068$$aSchiffer, Christian$$b0$$eCorresponding author$$ufzj
000888547 245__ $$aContrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections
000888547 260__ $$c2020
000888547 3367_ $$0PUB:(DE-HGF)25$$2PUB:(DE-HGF)$$aPreprint$$bpreprint$$mpreprint$$s1700723565_30560
000888547 3367_ $$2ORCID$$aWORKING_PAPER
000888547 3367_ $$028$$2EndNote$$aElectronic Article
000888547 3367_ $$2DRIVER$$apreprint
000888547 3367_ $$2BibTeX$$aARTICLE
000888547 3367_ $$2DataCite$$aOutput Types/Working Paper
000888547 500__ $$aPreprint submitted to ISBI 2021
000888547 520__ $$aCytoarchitectonic maps provide microstructural reference parcellations of the brain, describing its organization in terms of the spatial arrangement of neuronal cell bodies as measured from histological tissue sections. Recent work provided the first automatic segmentations of cytoarchitectonic areas in the visual system using Convolutional Neural Networks. We aim to extend this approach to become applicable to a wider range of brain areas, envisioning a solution for mapping the complete human brain. Inspired by recent success in image classification, we propose a contrastive learning objective for encoding microscopic image patches into robust microstructural features, which are efficient for cytoarchitectonic area classification. We show that a model pre-trained using this learning task outperforms a model trained from scratch, as well as a model pre-trained on a recently proposed auxiliary task. We perform cluster analysis in the feature space to show that the learned representations form anatomically meaningful groups.
000888547 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x0
000888547 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x1
000888547 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x2
000888547 536__ $$0G:(DE-Juel-1)E.40401.62$$aHelmholtz AI - Helmholtz Artificial Intelligence  Coordination Unit – Local Unit FZJ (E.40401.62)$$cE.40401.62$$x3
000888547 588__ $$aDataset connected to arXivarXiv
000888547 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b1$$ufzj
000888547 7001_ $$0P:(DE-HGF)0$$aHarmeling, Stefan$$b2
000888547 7001_ $$0P:(DE-Juel1)165746$$aDickscheid, Timo$$b3$$ufzj
000888547 8564_ $$uhttps://juser.fz-juelich.de/record/888547/files/Schiffer_etal_biorXiv_ISBI_2020_preprint.pdf$$yOpenAccess
000888547 909CO $$ooai:juser.fz-juelich.de:888547$$popenaire$$pdnbdelivery$$pec_fundedresources$$pVDB$$pdriver$$popen_access
000888547 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)170068$$aForschungszentrum Jülich$$b0$$kFZJ
000888547 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131631$$aForschungszentrum Jülich$$b1$$kFZJ
000888547 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165746$$aForschungszentrum Jülich$$b3$$kFZJ
000888547 9131_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x0
000888547 9141_ $$y2020
000888547 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000888547 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
000888547 980__ $$apreprint
000888547 980__ $$aVDB
000888547 980__ $$aI:(DE-Juel1)INM-1-20090406
000888547 980__ $$aUNRESTRICTED
000888547 9801_ $$aFullTexts