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dc.contributor.authorUtpala, Saiteja
dc.contributor.authorMiolane, Nina
dc.contributor.editorMunday, Sara
dc.contributor.editorRandecker, Anja
dc.date.accessioned2022-10-25T09:28:10Z
dc.date.available2022-10-25T09:28:10Z
dc.date.issued2022-10-25
dc.identifier.urihttp://publications.mfo.de/handle/mfo/3985
dc.description.abstractThe advances in biomedical imaging techniques have enabled us to access the 3D shapes of a variety of structures: organs, cells, proteins. Since biological shapes are related to physiological functions, shape data may hold the key to unlocking outstanding mysteries in biomedicine. This snapshot introduces the mathematical framework of geometric statistics and learning and its applications to biomedicine.en_US
dc.language.isoenen_US
dc.publisherMathematisches Forschungsinstitut Oberwolfachen_US
dc.relation.ispartofseriesSnapshots of modern mathematics from Oberwolfach;2022-08
dc.rightsAttribution-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/*
dc.titleBiological shape analysis with geometric statistics and learningen_US
dc.typeArticleen_US
dc.identifier.doi10.14760/SNAP-2022-008-EN
local.series.idSNAP-2022-008-ENen_US
local.subject.snapshotGeometry and Topologyen_US
local.subject.snapshotProbability Theory and Statisticsen_US
dc.identifier.urnurn:nbn:de:101:1-2022112209312693372541
dc.identifier.ppn1823161618


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