{"id":"https://openalex.org/W4416513077","doi":"https://doi.org/10.1109/access.2025.3636044","title":"Multi-Task Contrastive Learning for Skin Lesion Classification and Segmentation","display_name":"Multi-Task Contrastive Learning for Skin Lesion Classification and Segmentation","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416513077","doi":"https://doi.org/10.1109/access.2025.3636044"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3636044","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3636044","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3636044","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041930562","display_name":"W. Zhang","orcid":"https://orcid.org/0000-0002-4631-4991"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Wenxu Zhang","raw_affiliation_strings":["Department of Electrical Engineering, City University of Hong Kong, Hong Kong Special Administrative Region, Kowloon Tong, China"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, City University of Hong Kong, Hong Kong Special Administrative Region, Kowloon Tong, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050968035","display_name":"Zhaozheng Chen","orcid":"https://orcid.org/0000-0002-7946-1111"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Zhaozheng Chen","raw_affiliation_strings":["Department of Electrical Engineering, City University of Hong Kong, Hong Kong Special Administrative Region, Kowloon Tong, China"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, City University of Hong Kong, Hong Kong Special Administrative Region, Kowloon Tong, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042601939","display_name":"Albert Kai-Sun Wong","orcid":"https://orcid.org/0000-0001-7923-5854"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]},{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]},{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Albert Kai-Sun Wong","raw_affiliation_strings":["Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region, Kowloon, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region, Kowloon, China","institution_ids":["https://openalex.org/I168719708","https://openalex.org/I200769079","https://openalex.org/I889458895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051126889","display_name":"Bernard Chiu","orcid":"https://orcid.org/0000-0001-5237-2410"},"institutions":[{"id":"https://openalex.org/I75381157","display_name":"Wilfrid Laurier University","ror":"https://ror.org/00fn7gb05","country_code":"CA","type":"education","lineage":["https://openalex.org/I75381157"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Bernard Chiu","raw_affiliation_strings":["Department of Computer Science &#x0026; Physics, Wilfrid Laurier University, Waterloo, ON, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science &#x0026; Physics, Wilfrid Laurier University, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I75381157"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5041930562"],"corresponding_institution_ids":["https://openalex.org/I168719708"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.50438311,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"202171","last_page":"202185"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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/T11306","display_name":"Nonmelanoma Skin Cancer Studies","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"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":0.0003000000142492354,"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":0.8256000280380249},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.65420001745224},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6463000178337097},{"id":"https://openalex.org/keywords/jaccard-index","display_name":"Jaccard index","score":0.5569999814033508},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5314000248908997},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49239999055862427},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.44209998846054077},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.41519999504089355}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8256000280380249},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7534999847412109},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.739799976348877},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.65420001745224},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6463000178337097},{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.5569999814033508},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5314000248908997},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49239999055862427},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.44209998846054077},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.41519999504089355},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4106000065803528},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.39750000834465027},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.36559998989105225},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.3587000072002411},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.34049999713897705},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.32170000672340393},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3156000077724457},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.31540000438690186},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.30970001220703125},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2815000116825104},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.27300000190734863},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2529999911785126}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2025.3636044","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3636044","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f6a073911eef45608350dbb738bd8535","is_oa":true,"landing_page_url":"https://doaj.org/article/f6a073911eef45608350dbb738bd8535","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 202171-202185 (2025)","raw_type":"article"},{"id":"pmh:oai:pure.atira.dk:publications/30f14874-1a07-41cf-95f6-a1463ab17c4c","is_oa":true,"landing_page_url":"https://hdl.handle.net/2031/30f14874-1a07-41cf-95f6-a1463ab17c4c","pdf_url":null,"source":{"id":"https://openalex.org/S7407055387","display_name":"CityU Scholars","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ZHANG, W, CHEN, Z, WONG, A K-S & CHIU, B 2025, 'Multi-Task Contrastive Learning for Skin Lesion Classification and Segmentation', IEEE Access, vol. 13, pp. 202171-202185. https://doi.org/10.1109/ACCESS.2025.3636044","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3636044","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3636044","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"skin":[1,20,273],"lesion":[2,83,155,179,248],"classification":[3,62,91,183,211,240],"and":[4,14,17,27,39,46,63,156,207,212,237,242,269],"segmentation":[5,64,123,129,166,191,213,249],"from":[6,68,76,181,189],"dermoscopic":[7],"images":[8],"are":[9,110,118],"essential":[10],"for":[11,42,235,247],"the":[12,51,85,90,105,122,165,177,182,190,198,204,224,229,243,252],"early":[13],"effective":[15],"diagnosis":[16,268],"management":[18],"of":[19,87,272],"cancer.":[21],"This":[22],"study":[23],"aims":[24],"to":[25,101,113,121,161],"develop":[26],"evaluate":[28],"a":[29,56,69,77,148,171],"multi-task":[30,149],"convolutional":[31,96],"neural":[32],"network":[33],"(CNN)":[34],"that":[35,80,175],"integrates":[36],"contrastive":[37,150],"learning":[38],"attention":[40,53,98],"mechanisms":[41],"improved":[43,210],"feature":[44],"representations":[45,188],"task-specific":[47],"performance.":[48],"We":[49],"propose":[50],"convolution":[52],"(CA)":[54],"Y-Net,":[55],"dual-branch":[57],"CNN":[58],"architecture":[59],"with":[60,134,185,215],"separate":[61],"branches":[65],"decoding":[66,139],"features":[67],"shared":[70],"encoder.":[71],"Lesion":[72],"type":[73],"is":[74,93,131],"predicted":[75],"global":[78,178],"representation":[79,180],"captures":[81],"salient":[82],"features,":[84,116],"generation":[86],"which":[88,117,153],"in":[89,164,275],"branch":[92,184],"guided":[94],"by":[95,260],"block":[97],"modules":[99],"(CBAMs)":[100],"enhance":[102],"focus":[103],"on":[104,197],"most":[106],"informative":[107],"regions.":[108],"CBAMs":[109,208],"also":[111],"applied":[112],"highlight":[114],"encoder":[115],"then":[119],"passed":[120],"decoder":[124,130],"via":[125],"skip":[126],"connections.":[127],"The":[128,193,256],"further":[132,218],"enhanced":[133],"deep":[135,216],"supervision":[136,217],"at":[137],"multiple":[138],"stages.":[140],"To":[141],"jointly":[142],"optimize":[143],"both":[144],"tasks,":[145],"we":[146],"introduce":[147],"(MuCo)":[151],"loss,":[152],"contrasts":[154,176],"background":[157,187],"representations.":[158],"In":[159],"addition":[160],"pixel-level":[162],"contrast":[163],"encoder,":[167],"MuCo":[168,205],"loss":[169,206],"introduces":[170],"novel":[172],"cross-branch":[173],"component":[174],"local":[186],"branch.":[192],"model":[194],"was":[195],"evaluated":[196],"ISIC":[199],"2017":[200],"challenge":[201],"dataset.":[202],"Both":[203],"independently":[209],"performance,":[214],"enhancing":[219],"segmentation.":[220],"CA":[221,261],"Y-Net":[222,262],"achieved":[223],"highest":[225,244],"mean":[226],"area":[227],"under":[228],"receiver":[230],"operating":[231],"characteristic":[232],"curve":[233],"(AUC)":[234],"melanoma":[236],"seborrheic":[238],"keratosis":[239],"(92.8%)":[241],"Jaccard":[245],"Index":[246],"(79.6%)":[250],"among":[251],"state-of-the-art":[253],"methods":[254],"compared.":[255],"high":[257],"performance":[258],"afforded":[259],"could":[263],"potentially":[264],"enable":[265],"more":[266],"accurate":[267],"quantitative":[270],"monitoring":[271],"lesions":[274],"clinical":[276],"applications.":[277]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-23T00:00:00"}
