{"id":"https://openalex.org/W2976443263","doi":"https://doi.org/10.1145/3354031.3354050","title":"Retinal Artery/Vein Classification via Rotation Augmentation and Deeply Supervised U-net Segmentation","display_name":"Retinal Artery/Vein Classification via Rotation Augmentation and Deeply Supervised U-net Segmentation","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2976443263","doi":"https://doi.org/10.1145/3354031.3354050","mag":"2976443263"},"language":"en","primary_location":{"id":"doi:10.1145/3354031.3354050","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3354031.3354050","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 4th International Conference on Biomedical Signal and Image Processing (ICBIP 2019) - ICBIP '19","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/A5061131295","display_name":"Zhaolei Wang","orcid":"https://orcid.org/0000-0001-7121-6237"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaolei Wang","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Junbin Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junbin Lin","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100707149","display_name":"Ruixuan Wang","orcid":"https://orcid.org/0000-0002-8714-0369"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruixuan Wang","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108050904","display_name":"Wei\u2010Shi Zheng","orcid":"https://orcid.org/0000-0001-8327-0003"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weishi Zheng","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"71","last_page":"76"},"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":0.9955999851226807,"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":0.9934999942779541,"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/segmentation","display_name":"Segmentation","score":0.7724786996841431},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7398965954780579},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.708138644695282},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5426843762397766},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5326122045516968},{"id":"https://openalex.org/keywords/retinal-artery","display_name":"Retinal Artery","score":0.4687231779098511},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45414021611213684},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.45130690932273865},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4252711832523346},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3411172330379486},{"id":"https://openalex.org/keywords/retinal","display_name":"Retinal","score":0.2471553087234497},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.12121549248695374}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7724786996841431},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7398965954780579},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.708138644695282},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5426843762397766},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5326122045516968},{"id":"https://openalex.org/C2910707947","wikidata":"https://www.wikidata.org/wiki/Q489722","display_name":"Retinal Artery","level":3,"score":0.4687231779098511},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45414021611213684},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.45130690932273865},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4252711832523346},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3411172330379486},{"id":"https://openalex.org/C2780827179","wikidata":"https://www.wikidata.org/wiki/Q422001","display_name":"Retinal","level":2,"score":0.2471553087234497},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.12121549248695374},{"id":"https://openalex.org/C118487528","wikidata":"https://www.wikidata.org/wiki/Q161437","display_name":"Ophthalmology","level":1,"score":0.0},{"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":1,"locations":[{"id":"doi:10.1145/3354031.3354050","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3354031.3354050","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 4th International Conference on Biomedical Signal and Image Processing (ICBIP 2019) - ICBIP '19","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2117935664","https://openalex.org/W2137055403","https://openalex.org/W2139583008","https://openalex.org/W2150769593","https://openalex.org/W2174711832","https://openalex.org/W2270168057","https://openalex.org/W2526009326","https://openalex.org/W2618884931","https://openalex.org/W2750113637","https://openalex.org/W2800539275","https://openalex.org/W2805657784","https://openalex.org/W2890285265","https://openalex.org/W2891656998","https://openalex.org/W2963606038","https://openalex.org/W3103835616"],"related_works":["https://openalex.org/W2517104666","https://openalex.org/W2005437358","https://openalex.org/W2790662084","https://openalex.org/W1669643531","https://openalex.org/W2008656436","https://openalex.org/W2134924024","https://openalex.org/W2023558673","https://openalex.org/W2960184797","https://openalex.org/W4285827401","https://openalex.org/W3104734424"],"abstract_inverted_index":{"Automatic":[0],"classification":[1,51,132],"of":[2,23,82],"artery":[3],"and":[4,54,133],"vein":[5],"vessels":[6,25],"in":[7,78],"retinal":[8,24],"images":[9],"is":[10,97],"still":[11],"a":[12,127],"challenging":[13],"task.":[14],"Recent":[15],"work":[16],"mainly":[17],"focuses":[18],"on":[19,107,112,120],"the":[20,49,80,91,104],"graph":[21],"analysis":[22],"or":[26,43,101],"intensity":[27],"based":[28,119],"feature":[29],"extraction.":[30],"In":[31,85],"this":[32],"study,":[33],"we":[34],"use":[35],"one":[36],"stage":[37],"multiclass":[38],"segmentation":[39,118],"without":[40],"any":[41],"graph-based":[42],"vote-based":[44],"post":[45],"processing":[46],"to":[47,100],"solve":[48],"artery/vein":[50,83],"problem":[52],"directly":[53],"effectively.":[55],"We":[56],"experimentally":[57],"showed":[58],"that":[59,116],"with":[60,88,137],"limited":[61],"training":[62],"data,":[63],"data":[64],"augmentation":[65],"may":[66],"be":[67,124,135],"at":[68],"least":[69],"as":[70,72,126],"crucial":[71],"designing":[73],"complicated":[74],"deep":[75,121],"model":[76],"architectures":[77],"improving":[79],"performance":[81],"classification.":[84],"particular,":[86],"simply":[87],"rotation":[89],"augmentation,":[90],"popular":[92],"deeply":[93],"supervised":[94],"U-Net":[95],"(DS-Unet)":[96],"already":[98],"comparable":[99],"even":[102],"outperforms":[103],"state-of-the-art":[105],"methods":[106,139],"DRIVE":[108],"dataset.":[109],"Our":[110],"experiments":[111],"two":[113],"datasets":[114],"show":[115],"artery-vein-background":[117],"learning":[122],"can":[123,134],"used":[125],"promising":[128],"method":[129],"for":[130,140],"arteriovenous":[131],"combined":[136],"conventional":[138],"better":[141],"results.":[142]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-19T07:52:34.831488","created_date":"2025-10-10T00:00:00"}
