{"id":"https://openalex.org/W7024434629","doi":null,"title":"Representation learning for single cell morphological phenotyping","display_name":"Representation learning for single cell morphological phenotyping","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W7024434629"},"language":"en","primary_location":{"id":"pmh:oai:DiVA.org:umu-196839","doi":null,"is_oa":true,"landing_page_url":"http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-196839","pdf_url":null,"source":{"id":"https://openalex.org/S4306401559","display_name":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I86987016","host_organization_name":"KTH Royal Institute of Technology","host_organization_lineage":["https://openalex.org/I86987016"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/bachelorThesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-196839","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Nenner, Andreas","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Nenner, Andreas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.0008999999845400453,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11897","display_name":"Digital Holography and Microscopy","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/representation-learning","display_name":"representation learning","score":0.8830000162124634},{"id":"https://openalex.org/keywords/cell-morphology","display_name":"cell morphology","score":0.7300000190734863},{"id":"https://openalex.org/keywords/cell-phenotyping","display_name":"cell phenotyping","score":0.6449999809265137},{"id":"https://openalex.org/keywords/apoptosis","display_name":"apoptosis","score":0.5260000228881836},{"id":"https://openalex.org/keywords/image-based-assay","display_name":"image-based assay","score":0.765999972820282},{"id":"https://openalex.org/keywords/autoencoder","display_name":"autoencoder","score":0.7049999833106995},{"id":"https://openalex.org/keywords/variational-autoencoder","display_name":"variational autoencoder","score":1.0},{"id":"https://openalex.org/keywords/latent-representations","display_name":"latent representations","score":0.6000000238418579},{"id":"https://openalex.org/keywords/skov-3-cells","display_name":"SKOV3 cells","score":0.8009999990463257},{"id":"https://openalex.org/keywords/ovarian-cancer","display_name":"ovarian cancer","score":0.7229999899864197},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.7319999933242798},{"id":"https://openalex.org/keywords/logistic-regression-classifier","display_name":"logistic regression classifier","score":0.46299999952316284}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7706999778747559},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7605000138282776},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.7063000202178955},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.685699999332428},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.6435999870300293},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5950999855995178},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5717999935150146},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5647000074386597},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.548799991607666},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4684000015258789},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3736000061035156},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3684999942779541},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3564000129699707},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.26579999923706055},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2621000111103058},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:DiVA.org:umu-196839","doi":null,"is_oa":true,"landing_page_url":"http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-196839","pdf_url":null,"source":{"id":"https://openalex.org/S4306401559","display_name":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I86987016","host_organization_name":"KTH Royal Institute of Technology","host_organization_lineage":["https://openalex.org/I86987016"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/bachelorThesis"}],"best_oa_location":{"id":"pmh:oai:DiVA.org:umu-196839","doi":null,"is_oa":true,"landing_page_url":"http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-196839","pdf_url":null,"source":{"id":"https://openalex.org/S4306401559","display_name":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I86987016","host_organization_name":"KTH Royal Institute of Technology","host_organization_lineage":["https://openalex.org/I86987016"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/bachelorThesis"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4214805739","https://openalex.org/W4327547221","https://openalex.org/W4405170613","https://openalex.org/W4288368278","https://openalex.org/W4417233272","https://openalex.org/W4380029705","https://openalex.org/W3160954065","https://openalex.org/W4409316473","https://openalex.org/W4366505496","https://openalex.org/W7143296295"],"abstract_inverted_index":{"Preclinical":[0],"research":[1],"for":[2,209,266],"developing":[3],"new":[4],"drugs":[5],"is":[6,264],"a":[7,63,118,162,171,176,211,237],"long":[8],"and":[9,17,24,50,105,128,157,206,224],"expensive":[10],"procedure.":[11],"Experiments":[12],"relying":[13],"on":[14,38,74,90,170],"image":[15],"acquisition":[16],"analysis":[18],"tend":[19],"to":[20,61,94,130,187,246,269],"be":[21,62],"low":[22],"throughput":[23],"use":[25],"reporter":[26],"systems":[27],"that":[28,262],"may":[29],"influence":[30],"the":[31,167,185,220,257],"studied":[32],"cells.":[33,145,195],"With":[34],"image-based":[35],"assays":[36],"focusing":[37],"extracting":[39],"qualitative":[40],"information":[41],"from":[42],"microscopic":[43],"images":[44,190],"of":[45,66,79,121,191],"mammalian":[46],"cells,":[47],"more":[48],"cost-efficient":[49],"high-throughput":[51],"analyses":[52],"are":[53,88,153],"possible.":[54],"Furthermore,":[55],"studying":[56],"cell":[57,67,75,80,91,122,231,278],"morphology":[58],"has":[59,82],"proven":[60],"good":[64],"indicator":[65],"phenotype.":[68],"Using":[69],"hand-crafted":[70,86,138,221,271],"feature":[71,222,272],"descriptors":[72,87,273],"based":[73,89,169],"morphology,":[76],"label-free":[77],"quantification":[78],"apoptosis":[81],"been":[83],"achieved.":[84],"These":[85],"characteristics":[92],"translated":[93],"quantifiable":[95],"metrics,":[96],"but":[97,239],"risk":[98],"being":[99],"biased":[100],"towards":[101],"easily":[102],"observable":[103],"features":[104,123],"therefore":[106],"miss":[107],"subtle":[108],"ones.":[109],"This":[110],"project":[111],"proposes":[112],"an":[113],"alternative":[114],"approach":[115],"by":[116],"generating":[117],"latent":[119,177,197],"representation":[120,198,226],"using":[124,175],"deep":[125,150],"learning":[126,151],"models":[127,152,168,186],"aims":[129],"find":[131],"if":[132],"they":[133],"can":[134],"compete":[135],"with":[136,179,229],"pre-defined":[137],"representations":[139,223,235,268],"in":[140,242,256,274],"classifying":[141],"live":[142,275],"or":[143,276],"dead":[144,277],"For":[146],"this":[147],"purpose,":[148],"three":[149],"implemented,":[154],"one":[155],"autoencoder":[156],"two":[158],"variational-autoencoder.":[159],"We":[160,182],"develop":[161],"core":[163],"architecture":[164],"shared":[165],"between":[166,219],"convolutional":[172],"neural":[173],"network":[174],"space":[178],"16":[180],"dimensions.":[181],"then":[183],"train":[184],"recreate":[188],"single-cell":[189],"SKOV3":[192],"ovarian":[193],"cancer":[194],"The":[196,233],"was":[199,227],"extracted":[200],"at":[201],"specific":[202],"checkpoints":[203],"during":[204],"training":[205,210],"later":[207],"used":[208],"logistic":[212],"regression":[213],"classifier.":[214],"Finally,":[215],"comparing":[216],"classification":[217,243],"accuracy":[218],"generated":[225,234,267],"made":[228],"novel":[230],"images.":[232],"show":[236],"slight":[238],"consistent":[240],"increase":[241],"accuracy,":[244],"up":[245],"4.9":[247],"percent":[248],"points,":[249],"even":[250],"without":[251],"capturing":[252],"all":[253],"morphological":[254],"details":[255],"recreation.":[258],"Thus,":[259],"we":[260],"conclude":[261],"it":[263],"possible":[265],"outperform":[270],"classification.":[279]},"counts_by_year":[],"updated_date":"2026-08-27T14:10:00.468798","created_date":"2025-10-10T00:00:00"}
