{"id":"https://openalex.org/W4417117279","doi":"https://doi.org/10.1109/isbi61048.2026.11515611","title":"High-Quality Synthetic Annotated Tissue Data Using Conditional Generative Adversarial Networks","display_name":"High-Quality Synthetic Annotated Tissue Data Using Conditional Generative Adversarial Networks","publication_year":2025,"publication_date":"2025-12-05","ids":{"openalex":"https://openalex.org/W4417117279","doi":"https://doi.org/10.1109/isbi61048.2026.11515611"},"language":"en","primary_location":{"id":"doi:10.1109/isbi61048.2026.11515611","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi61048.2026.11515611","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.64898/2025.12.04.690437","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Siddharth Srivastava","orcid":"https://orcid.org/0009-0009-5667-8287"},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Siddharth Srivastava","raw_affiliation_strings":["University of Warwick;"],"raw_orcid":"https://orcid.org/0009-0009-5667-8287","affiliations":[{"raw_affiliation_string":"University of Warwick;","institution_ids":["https://openalex.org/I39555362"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043042507","display_name":"Cornelis J. Weijer","orcid":"https://orcid.org/0000-0003-2192-8150"},"institutions":[{"id":"https://openalex.org/I177639307","display_name":"University of Dundee","ror":"https://ror.org/03h2bxq36","country_code":"GB","type":"education","lineage":["https://openalex.org/I177639307"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Cornelis Jan Weijer","raw_affiliation_strings":["University of Dundee"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Dundee","institution_ids":["https://openalex.org/I177639307"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074864006","display_name":"Till Bretschneider","orcid":"https://orcid.org/0000-0002-5317-603X"},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Till Bretschneider","raw_affiliation_strings":["University of Warwick;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Warwick;","institution_ids":["https://openalex.org/I39555362"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I39555362"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36067198,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.47200000286102295,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.47200000286102295,"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/T11289","display_name":"Single-cell and spatial transcriptomics","score":0.061500001698732376,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.05609999969601631,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.8378999829292297},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6850000023841858},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6283000111579895},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.578499972820282},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.46869999170303345},{"id":"https://openalex.org/keywords/translation","display_name":"Translation (biology)","score":0.4523000121116638},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.45010000467300415},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3792000114917755}],"concepts":[{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.8378999829292297},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7682999968528748},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6850000023841858},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6628000140190125},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6283000111579895},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.578499972820282},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.46869999170303345},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.4523000121116638},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.45010000467300415},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4203999936580658},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3792000114917755},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.352400004863739},{"id":"https://openalex.org/C2781235140","wikidata":"https://www.wikidata.org/wiki/Q275131","display_name":"Scratch","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3366999924182892},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.32589998841285706},{"id":"https://openalex.org/C129536746","wikidata":"https://www.wikidata.org/wiki/Q1141026","display_name":"Gastrulation","level":4,"score":0.3059000074863434},{"id":"https://openalex.org/C2780977526","wikidata":"https://www.wikidata.org/wiki/Q42417149","display_name":"Data exploration","level":3,"score":0.2793000042438507},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2703000009059906},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.26100000739097595}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/isbi61048.2026.11515611","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi61048.2026.11515611","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"},{"id":"doi:10.64898/2025.12.04.690437","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2025.12.04.690437","pdf_url":null,"source":{"id":"https://openalex.org/S4306402567","display_name":"bioRxiv (Cold Spring Harbor Laboratory)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2750212522","host_organization_name":"Cold Spring Harbor Laboratory","host_organization_lineage":["https://openalex.org/I2750212522"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"bioRxiv","raw_type":"posted-content"},{"id":"pmh:oai:wrap.warwick.ac.uk:196112","is_oa":true,"landing_page_url":null,"pdf_url":"https://wrap.warwick.ac.uk/id/eprint/196112/3/WRAP-High-quality-synthetic-annotated-tissue-data-conditional-generative-26.pdf","source":{"id":"https://openalex.org/S4306400665","display_name":"Warwick Research Archive Portal (University of Warwick)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I39555362","host_organization_name":"University of Warwick","host_organization_lineage":["https://openalex.org/I39555362"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"NonPeerReviewed"}],"best_oa_location":{"id":"doi:10.64898/2025.12.04.690437","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2025.12.04.690437","pdf_url":null,"source":{"id":"https://openalex.org/S4306402567","display_name":"bioRxiv (Cold Spring Harbor Laboratory)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2750212522","host_organization_name":"Cold Spring Harbor Laboratory","host_organization_lineage":["https://openalex.org/I2750212522"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"bioRxiv","raw_type":"posted-content"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6037048461","display_name":"CMMI-EPSRC: A novel multifunctional platform to study cell and nuclear mechanosensing","funder_award_id":"EP/X026663/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G7111222371","display_name":null,"funder_award_id":"EP/V062522/1,EP/X026663/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G8685503513","display_name":"Machine learning for extracting spatio-temporal biological patterns on evolving domains","funder_award_id":"EP/V062522/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2027797725","https://openalex.org/W2623550831","https://openalex.org/W2775908481","https://openalex.org/W2982006073","https://openalex.org/W2989859439","https://openalex.org/W3111521801","https://openalex.org/W4311776816","https://openalex.org/W4313544866"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"We":[1,21,57],"present":[2],"a":[3,46,105],"deep-learning":[4],"based":[5],"pipeline":[6],"for":[7],"generating":[8],"high-quality":[9],"synthetic":[10,38,64,84,99,129],"instance-segmentation":[11],"datasets":[12,85],"of":[13,62,91],"tissues":[14],"undergoing":[15],"early":[16],"gastrulation":[17,118],"in":[18],"chick":[19],"embryo.":[20],"create":[22,37],"point-clouds":[23,35],"using":[24,45],"the":[25,52,59,83,88,92],"Lennard-Jones":[26],"potential":[27],"and":[28,41,69,77,95,124,138],"learn":[29],"an":[30],"image-to-image":[31],"translation":[32],"from":[33,50,72,135],"these":[34],"to":[36,122],"tissue":[39],"images":[40],"auxiliary":[42],"flow":[43],"tensors":[44],"Generative":[47],"Adversarial":[48],"Network,":[49],"which":[51],"segmentation":[53,102],"masks":[54],"are":[55,120],"derived.":[56],"evaluate":[58],"downstream":[60],"utility":[61],"our":[63,98],"data":[65,75,78,100,119],"by":[66],"training":[67],"Cellpose":[68],"Stardist":[70],"models":[71],"scratch":[73],"under":[74],"replacement":[76],"augmentation":[79],"scenarios,":[80],"show":[81,96],"that":[82,97],"effectively":[86],"capture":[87],"statistical":[89],"properties":[90],"real":[93],"dataset,":[94],"improves":[101],"performance":[103],"on":[104],"held-out":[106],"test":[107],"set.":[108],"This":[109],"approach":[110],"substantially":[111],"reduces":[112],"expert":[113],"annotation":[114],"time,":[115],"as":[116],"late-stage":[117],"challenging":[121],"acquire":[123],"manually":[125],"label,":[126],"while":[127],"similar":[128],"examples":[130],"can":[131],"be":[132],"flexibly":[133],"generated":[134],"easily":[136],"obtainable":[137],"annotated":[139],"early-stage":[140],"data.":[141],"Code:":[142],"https://gitlab.com/siddharthsrivastava/synthetic-tissue-data":[143]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-12-08T00:00:00"}