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dc.contributor.authorAli, Mohammed A. S.
dc.contributor.authorHollo, Kaspar
dc.contributor.authorLaasfeld, Tõnis
dc.contributor.authorTorp, Jane
dc.contributor.authorTahk, Maris-Johanna
dc.contributor.authorRinken, Ago
dc.contributor.authorPalo, Kaupo
dc.contributor.authorParts, Leopold
dc.contributor.authorFishman, Dmytro
dc.coverage.spatialUniversity of Tartu, Institute of Computer Sciencesen
dc.date.accessioned2022-01-24T08:10:28Z
dc.date.available2022-01-24T08:10:28Z
dc.date.issued2022-01-24
dc.identifier.urihttps://datadoi.ee/handle/33/433
dc.identifier.urihttps://doi.org/10.23673/re-307
dc.description.abstractThe "ArtSeg-CHO-M4R, artifact segmentation in microscopy of ligand binding to M4 muscarinic receptor in live CHO-K1-hM4 cells" dataset contains microscopy images along with the ground truth binary masks for artifact segmentation from brightfield images. The dataset consists of three main directories for train, validation, and test splits. The images' datatype is an unsigned integer (uint8) with a range from 0 and 255 while the ground truth masks pixel values are 0 or 1.en
dc.formatPNGen
dc.language.isoenen
dc.publisherUniversity of Tartu, Institute of Computer Sciencesen
dc.relationhttp://dx.doi.org/10.23673/re-306
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.rightsAttribution 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectartifacten
dc.subjectartifact segmentationen
dc.subjectanomaly removalen
dc.subjectimage analysisen
dc.subjectdeep learningen
dc.subjectbrightfield microscopyen
dc.titleArtSeg-CHO-M4R: artifact segmentation in microscopy of ligand binding to M4 muscarinic receptor in live CHO-K1-hM4 cells.en
dc.typeinfo:eu-repo/semantics/dataseten


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