{"id":"https://openalex.org/W4416249459","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227613","title":"Multi-depth transfer learning-based approaches via generative models for foveal avascular zone segmentation in OCTA images","display_name":"Multi-depth transfer learning-based approaches via generative models for foveal avascular zone segmentation in OCTA images","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416249459","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227613"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227613","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227613","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120499553","display_name":"\u00c1ngel Regueiro","orcid":null},"institutions":[{"id":"https://openalex.org/I4210160015","display_name":"Instituto de Investigaci\u00f3n Biom\u00e9dica de A Coru\u00f1a","ror":"https://ror.org/04c9g9234","country_code":"ES","type":"facility","lineage":["https://openalex.org/I4210160015"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"\u00c1ngel Regueiro","raw_affiliation_strings":["Universidade da Coruña,VARPA Research Group, Instituto de Investigación Biomédica de A Coruña (INIBIC),A Coruña,Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade da Coruña,VARPA Research Group, Instituto de Investigación Biomédica de A Coruña (INIBIC),A Coruña,Spain","institution_ids":["https://openalex.org/I4210160015"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071769211","display_name":"Elena Goyanes","orcid":"https://orcid.org/0009-0001-5328-2262"},"institutions":[{"id":"https://openalex.org/I4210160015","display_name":"Instituto de Investigaci\u00f3n Biom\u00e9dica de A Coru\u00f1a","ror":"https://ror.org/04c9g9234","country_code":"ES","type":"facility","lineage":["https://openalex.org/I4210160015"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Elena Goyanes","raw_affiliation_strings":["Universidade da Coruña,VARPA Research Group, Instituto de Investigación Biomédica de A Coruña (INIBIC),A Coruña,Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade da Coruña,VARPA Research Group, Instituto de Investigación Biomédica de A Coruña (INIBIC),A Coruña,Spain","institution_ids":["https://openalex.org/I4210160015"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081322996","display_name":"Joaquim de Moura","orcid":"https://orcid.org/0000-0002-2050-3786"},"institutions":[{"id":"https://openalex.org/I4210160015","display_name":"Instituto de Investigaci\u00f3n Biom\u00e9dica de A Coru\u00f1a","ror":"https://ror.org/04c9g9234","country_code":"ES","type":"facility","lineage":["https://openalex.org/I4210160015"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Joaquim de Moura","raw_affiliation_strings":["Universidade da Coruña,VARPA Research Group, Instituto de Investigación Biomédica de A Coruña (INIBIC),A Coruña,Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade da Coruña,VARPA Research Group, Instituto de Investigación Biomédica de A Coruña (INIBIC),A Coruña,Spain","institution_ids":["https://openalex.org/I4210160015"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032144370","display_name":"Jorge Novo","orcid":"https://orcid.org/0000-0002-0125-3064"},"institutions":[{"id":"https://openalex.org/I4210160015","display_name":"Instituto de Investigaci\u00f3n Biom\u00e9dica de A Coru\u00f1a","ror":"https://ror.org/04c9g9234","country_code":"ES","type":"facility","lineage":["https://openalex.org/I4210160015"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Jorge Novo","raw_affiliation_strings":["Universidade da Coruña,VARPA Research Group, Instituto de Investigación Biomédica de A Coruña (INIBIC),A Coruña,Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade da Coruña,VARPA Research Group, Instituto de Investigación Biomédica de A Coruña (INIBIC),A Coruña,Spain","institution_ids":["https://openalex.org/I4210160015"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077849552","display_name":"Marcos Ortega","orcid":"https://orcid.org/0000-0002-2798-0788"},"institutions":[{"id":"https://openalex.org/I4210160015","display_name":"Instituto de Investigaci\u00f3n Biom\u00e9dica de A Coru\u00f1a","ror":"https://ror.org/04c9g9234","country_code":"ES","type":"facility","lineage":["https://openalex.org/I4210160015"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Marcos Ortega","raw_affiliation_strings":["Universidade da Coruña,VARPA Research Group, Instituto de Investigación Biomédica de A Coruña (INIBIC),A Coruña,Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade da Coruña,VARPA Research Group, Instituto de Investigación Biomédica de A Coruña (INIBIC),A Coruña,Spain","institution_ids":["https://openalex.org/I4210160015"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210160015"],"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":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.986299991607666,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.986299991607666,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10170","display_name":"Retinal Diseases and Treatments","score":0.0035000001080334187,"subfield":{"id":"https://openalex.org/subfields/2731","display_name":"Ophthalmology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.0010000000474974513,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7215999960899353},{"id":"https://openalex.org/keywords/foveal","display_name":"Foveal","score":0.6427000164985657},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6226000189781189},{"id":"https://openalex.org/keywords/jaccard-index","display_name":"Jaccard index","score":0.5982000231742859},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5598000288009644},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5117999911308289},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4693000018596649},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4650999903678894},{"id":"https://openalex.org/keywords/foveal-avascular-zone","display_name":"Foveal avascular zone","score":0.4494999945163727}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7583000063896179},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7436000108718872},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7215999960899353},{"id":"https://openalex.org/C30181142","wikidata":"https://www.wikidata.org/wiki/Q865103","display_name":"Foveal","level":3,"score":0.6427000164985657},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6226000189781189},{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.5982000231742859},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5598000288009644},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5117999911308289},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4693000018596649},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4650999903678894},{"id":"https://openalex.org/C2777655287","wikidata":"https://www.wikidata.org/wiki/Q28455839","display_name":"Foveal avascular zone","level":4,"score":0.4494999945163727},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.44690001010894775},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42570000886917114},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.42289999127388},{"id":"https://openalex.org/C2778818243","wikidata":"https://www.wikidata.org/wiki/Q899552","display_name":"Optical coherence tomography","level":2,"score":0.415800005197525},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.41280001401901245},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.382099986076355},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3012000024318695},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.3003000020980835},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2824999988079071},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2721000015735626},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.263700008392334},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2574999928474426},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.2549000084400177},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227613","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227613","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322930","display_name":"Ministerio de Ciencia e Innovaci\u00f3n","ror":"https://ror.org/034900433"},{"id":"https://openalex.org/F4320326655","display_name":"Xunta de Galicia","ror":"https://ror.org/0181xnw06"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1977349774","https://openalex.org/W2108598243","https://openalex.org/W2160436805","https://openalex.org/W2326425955","https://openalex.org/W2462081900","https://openalex.org/W2463696354","https://openalex.org/W2554093589","https://openalex.org/W2613174633","https://openalex.org/W2775636951","https://openalex.org/W2807643268","https://openalex.org/W2888780751","https://openalex.org/W2900999361","https://openalex.org/W2993280523","https://openalex.org/W3126589190","https://openalex.org/W4286539715","https://openalex.org/W4289653580","https://openalex.org/W4402673604"],"related_works":[],"abstract_inverted_index":{"The":[0],"automatic":[1],"identification":[2],"and":[3,15,55,89,124,180,201],"segmentation":[4,150,199],"of":[5,23,94,100,169,176,184,191],"the":[6,20,95,98,105,117,189],"Foveal":[7],"Avascular":[8],"Zone":[9],"(FAZ)":[10],"is":[11,104],"crucial":[12],"for":[13,38],"diagnosing":[14],"monitoring":[16],"retinal":[17],"diseases.":[18],"However,":[19],"limited":[21,205],"availability":[22],"Optical":[24],"Coherence":[25],"Tomography":[26],"Angiography":[27],"(OCTA)":[28],"images":[29],"with":[30,204],"ground":[31],"truth":[32],"annotations":[33],"poses":[34],"a":[35,71,82,165,173,181],"significant":[36],"challenge":[37],"developing":[39],"robust":[40],"deep":[41,90],"learning":[42,46,75,195],"models.":[43],"Traditional":[44],"transfer":[45,74],"techniques,":[47],"such":[48],"as":[49],"ImageNet-based":[50],"pre-training,":[51],"require":[52],"large":[53],"datasets":[54],"struggle":[56],"to":[57,59,119,128,153],"adapt":[58],"domain-specific":[60,130],"tasks":[61],"in":[62,197],"medical":[63],"imaging.":[64],"To":[65,97],"address":[66],"this":[67,103],"issue,":[68],"we":[69],"propose":[70],"novel":[72],"multi-deep":[73],"framework":[76,141],"that":[77,108],"leverages":[78],"pretrained":[79],"models":[80],"on":[81],"generation":[83],"task":[84],"using":[85,158],"both":[86],"superficial":[87],"(SCP)":[88],"capillary":[91],"plexuses":[92],"(DCP)":[93],"retina.":[96],"best":[99],"our":[101,140,192],"knowledge,":[102],"first":[106],"approach":[107,163],"integrates":[109],"depth":[110],"information":[111],"from":[112],"multiple":[113],"vascular":[114,131],"plexuses,":[115],"allowing":[116],"model":[118],"capture":[120],"cross-plexus":[121],"structural":[122],"relationships":[123],"enhance":[125],"its":[126],"ability":[127],"learn":[129],"features":[132],"while":[133],"mitigating":[134],"data":[135,206],"scarcity":[136],"constraints.":[137],"We":[138],"validated":[139],"through":[142],"extensive":[143],"experiments,":[144],"demonstrating":[145],"competitive":[146],"or":[147],"even":[148,203],"superior":[149],"performance":[151],"compared":[152],"state-of-the-art":[154],"pretraining":[155],"models,":[156],"despite":[157],"significantly":[159],"smaller":[160],"datasets.":[161],"Our":[162],"achieved":[164],"Dice":[166],"coefficient":[167],"score":[168],"0.8238":[170],"\u00b1":[171,178,186],"0.0263,":[172],"Jaccard":[174],"Index":[175,183],"0.7189":[177],"0.0293,":[179],"Correlation":[182],"0.8328":[185],"0.0229,":[187],"highlighting":[188],"effectiveness":[190],"multi-depth":[193],"feature":[194],"strategy":[196],"improving":[198],"precision":[200],"generalization,":[202],"availability.":[207]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-14T00:00:00"}