{"id":"https://openalex.org/W2774995168","doi":"https://doi.org/10.1109/globalsip.2017.8309054","title":"Artery/vein classification in fundus images using CNN and likelihood score propagation","display_name":"Artery/vein classification in fundus images using CNN and likelihood score propagation","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2774995168","doi":"https://doi.org/10.1109/globalsip.2017.8309054","mag":"2774995168"},"language":"en","primary_location":{"id":"doi:10.1109/globalsip.2017.8309054","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2017.8309054","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","raw_type":"proceedings-article"},"type":"article","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/A5047190978","display_name":"Fantin Girard","orcid":"https://orcid.org/0000-0001-8620-9670"},"institutions":[{"id":"https://openalex.org/I45683168","display_name":"Polytechnique Montr\u00e9al","ror":"https://ror.org/05f8d4e86","country_code":"CA","type":"education","lineage":["https://openalex.org/I45683168"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Fantin Girard","raw_affiliation_strings":["Polytechnique Montreal, Montreal, QC, Canada"],"affiliations":[{"raw_affiliation_string":"Polytechnique Montreal, Montreal, QC, Canada","institution_ids":["https://openalex.org/I45683168"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072102193","display_name":"Farida Ch\u00e9riet","orcid":"https://orcid.org/0000-0001-6170-5627"},"institutions":[{"id":"https://openalex.org/I45683168","display_name":"Polytechnique Montr\u00e9al","ror":"https://ror.org/05f8d4e86","country_code":"CA","type":"education","lineage":["https://openalex.org/I45683168"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Farida Cheriet","raw_affiliation_strings":["Polytechnique Montreal, Montreal, QC, Canada"],"affiliations":[{"raw_affiliation_string":"Polytechnique Montreal, Montreal, QC, Canada","institution_ids":["https://openalex.org/I45683168"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5047190978"],"corresponding_institution_ids":["https://openalex.org/I45683168"],"apc_list":null,"apc_paid":null,"fwci":2.3535,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.88591745,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"abs 1412 6980","issue":null,"first_page":"720","last_page":"724"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":1.0,"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":1.0,"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/T12599","display_name":"Retinal and Optic Conditions","score":0.9987000226974487,"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/T10170","display_name":"Retinal Diseases and Treatments","score":0.995199978351593,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6885719299316406},{"id":"https://openalex.org/keywords/fundus","display_name":"Fundus (uterus)","score":0.6029418110847473},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6008671522140503},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5939246416091919},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46684014797210693},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4562862813472748},{"id":"https://openalex.org/keywords/artery","display_name":"Artery","score":0.44097277522087097},{"id":"https://openalex.org/keywords/retinal-artery","display_name":"Retinal Artery","score":0.4360164403915405},{"id":"https://openalex.org/keywords/retinal","display_name":"Retinal","score":0.36750656366348267},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.28512582182884216},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.2825104296207428},{"id":"https://openalex.org/keywords/cardiology","display_name":"Cardiology","score":0.2718493342399597},{"id":"https://openalex.org/keywords/ophthalmology","display_name":"Ophthalmology","score":0.13961052894592285}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6885719299316406},{"id":"https://openalex.org/C2776391266","wikidata":"https://www.wikidata.org/wiki/Q9612","display_name":"Fundus (uterus)","level":2,"score":0.6029418110847473},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6008671522140503},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5939246416091919},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46684014797210693},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4562862813472748},{"id":"https://openalex.org/C2776820930","wikidata":"https://www.wikidata.org/wiki/Q9655","display_name":"Artery","level":2,"score":0.44097277522087097},{"id":"https://openalex.org/C2910707947","wikidata":"https://www.wikidata.org/wiki/Q489722","display_name":"Retinal Artery","level":3,"score":0.4360164403915405},{"id":"https://openalex.org/C2780827179","wikidata":"https://www.wikidata.org/wiki/Q422001","display_name":"Retinal","level":2,"score":0.36750656366348267},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.28512582182884216},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.2825104296207428},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.2718493342399597},{"id":"https://openalex.org/C118487528","wikidata":"https://www.wikidata.org/wiki/Q161437","display_name":"Ophthalmology","level":1,"score":0.13961052894592285},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/globalsip.2017.8309054","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2017.8309054","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:publications.polymtl.ca:39702","is_oa":false,"landing_page_url":"https://publications.polymtl.ca/39702/","pdf_url":null,"source":{"id":"https://openalex.org/S4306401013","display_name":"PolyPublie (\u00c9cole Polytechnique de Montr\u00e9al)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I45683168","host_organization_name":"Polytechnique Montr\u00e9al","host_organization_lineage":["https://openalex.org/I45683168"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Communication de conf\u00e9rence"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.7699999809265137,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1473767589","https://openalex.org/W1522301498","https://openalex.org/W1533861849","https://openalex.org/W1901129140","https://openalex.org/W2039580825","https://openalex.org/W2100756624","https://openalex.org/W2112796928","https://openalex.org/W2117935664","https://openalex.org/W2137055403","https://openalex.org/W2163605009","https://openalex.org/W2174711832","https://openalex.org/W2270168057","https://openalex.org/W2402050431","https://openalex.org/W2964121744","https://openalex.org/W6631190155","https://openalex.org/W6631943919","https://openalex.org/W6660133353","https://openalex.org/W6684191040","https://openalex.org/W6713423098"],"related_works":["https://openalex.org/W2967742050","https://openalex.org/W2161712797","https://openalex.org/W1977918393","https://openalex.org/W1997229834","https://openalex.org/W2433129310","https://openalex.org/W2014359031","https://openalex.org/W2081941244","https://openalex.org/W2136690400","https://openalex.org/W2513578631","https://openalex.org/W106156095"],"abstract_inverted_index":{"Artery/vein":[0],"classification":[1,46,59,125],"in":[2],"fundus":[3],"images":[4],"is":[5,32,73,88,126],"a":[6,54,68,110],"prerequisite":[7],"for":[8,57,75],"the":[9,27,33,42,76,92,101,114,119,130,135,154,159,162],"assessment":[10],"of":[11,29,44,78,134,151,161,165],"diseases":[12],"such":[13],"as":[14,100],"diabetes,":[15],"hypertension":[16],"or":[17,49,84],"other":[18],"cardiovascular":[19,30],"pathologies.":[20],"One":[21],"clinical":[22],"measure":[23],"used":[24],"to":[25,109,158],"assess":[26],"severity":[28],"risk":[31],"retinal":[34,93],"arterio-venous":[35],"ratio":[36],"(AVR),":[37],"which":[38],"significantly":[39],"depends":[40],"on":[41,141,153],"accuracy":[43,150,164],"vessel":[45,80,102],"into":[47,82],"arteries":[48,83],"veins.":[50,85],"This":[51],"paper":[52],"proposes":[53],"novel":[55],"method":[56,140,147],"artery/vein":[58,124],"combining":[60],"deep":[61],"learning":[62],"and":[63,104],"graph":[64,87],"propagation":[65],"strategies.":[66],"First,":[67],"convolutional":[69],"neural":[70],"network":[71],"(CNN)":[72],"trained":[74],"task":[77],"labeling":[79],"pixels":[81],"A":[86],"then":[89],"constructed":[90],"from":[91],"vascular":[94],"network.":[95],"The":[96,122],"nodes":[97],"are":[98],"defined":[99],"branches":[103,116],"each":[105],"edge":[106],"gets":[107],"associated":[108],"cost":[111],"evaluating":[112],"if":[113],"two":[115,142],"should":[117],"have":[118],"same":[120],"label.":[121],"CNN's":[123],"efficiently":[127],"propagated":[128],"through":[129],"minimum":[131],"spanning":[132],"tree":[133],"graph.":[136],"We":[137],"validated":[138],"our":[139],"publicly":[143],"available":[144],"databases.":[145],"Our":[146],"achieves":[148],"an":[149],"93.3%":[152],"DRIVE":[155],"database":[156],"compared":[157],"state":[160],"art":[163],"91.7%.":[166]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}