{"id":"https://openalex.org/W4306831116","doi":"https://doi.org/10.1109/tmi.2022.3216005","title":"GraVIS: Grouping Augmented Views From Independent Sources for Dermatology Analysis","display_name":"GraVIS: Grouping Augmented Views From Independent Sources for Dermatology Analysis","publication_year":2022,"publication_date":"2022-11-03","ids":{"openalex":"https://openalex.org/W4306831116","doi":"https://doi.org/10.1109/tmi.2022.3216005","pmid":"https://pubmed.ncbi.nlm.nih.gov/36260573"},"language":"en","primary_location":{"id":"doi:10.1109/tmi.2022.3216005","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2022.3216005","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5100783219","display_name":"Hong-Yu Zhou","orcid":"https://orcid.org/0000-0002-1256-7050"},"institutions":[{"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"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Hong-Yu Zhou","raw_affiliation_strings":["Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0002-1256-7050","affiliations":[{"raw_affiliation_string":"Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086804572","display_name":"Chixiang Lu","orcid":"https://orcid.org/0000-0003-0665-2627"},"institutions":[{"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"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Chixiang Lu","raw_affiliation_strings":["Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0003-0665-2627","affiliations":[{"raw_affiliation_string":"Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100613491","display_name":"Liansheng Wang","orcid":"https://orcid.org/0000-0002-2096-454X"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liansheng Wang","raw_affiliation_strings":["Department of Computer Science, Xiamen University, Siming District, Xiamen, Fujian, China"],"raw_orcid":"https://orcid.org/0000-0002-2096-454X","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Xiamen University, Siming District, Xiamen, Fujian, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108557359","display_name":"Yizhou Yu","orcid":"https://orcid.org/0000-0002-0470-5548"},"institutions":[{"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"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yizhou Yu","raw_affiliation_strings":["Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0002-0470-5548","affiliations":[{"raw_affiliation_string":"Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong","institution_ids":["https://openalex.org/I889458895"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1194917,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"41","issue":"12","first_page":"3498","last_page":"3508"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9970999956130981,"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"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9970999956130981,"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"}},{"id":"https://openalex.org/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.9923999905586243,"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/T11316","display_name":"Mycobacterium research and diagnosis","score":0.9919000267982483,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6977825164794922},{"id":"https://openalex.org/keywords/homogeneous","display_name":"Homogeneous","score":0.6951438188552856},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6587434411048889},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.571821928024292},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.569595456123352},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5461704134941101},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47631391882896423},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.4702398180961609},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.46133148670196533},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4559209942817688},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.41345053911209106},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4119825065135956},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.38530486822128296},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18036764860153198},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1785600781440735}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6977825164794922},{"id":"https://openalex.org/C66882249","wikidata":"https://www.wikidata.org/wiki/Q169336","display_name":"Homogeneous","level":2,"score":0.6951438188552856},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6587434411048889},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.571821928024292},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.569595456123352},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5461704134941101},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47631391882896423},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4702398180961609},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.46133148670196533},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4559209942817688},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.41345053911209106},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4119825065135956},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.38530486822128296},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18036764860153198},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1785600781440735},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D003880","descriptor_name":"Dermatology","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D003880","descriptor_name":"Dermatology","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D003880","descriptor_name":"Dermatology","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D012660","descriptor_name":"Semantics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012660","descriptor_name":"Semantics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012660","descriptor_name":"Semantics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1109/tmi.2022.3216005","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2022.3216005","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},{"id":"pmid:36260573","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36260573","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on medical imaging","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.4000000059604645,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":75,"referenced_works":["https://openalex.org/W2096733369","https://openalex.org/W2118393783","https://openalex.org/W2144796873","https://openalex.org/W2152790380","https://openalex.org/W2194775991","https://openalex.org/W2467139031","https://openalex.org/W2519373641","https://openalex.org/W2555897561","https://openalex.org/W2594652617","https://openalex.org/W2594893939","https://openalex.org/W2597394795","https://openalex.org/W2598634450","https://openalex.org/W2602631755","https://openalex.org/W2604384166","https://openalex.org/W2774320778","https://openalex.org/W2798991696","https://openalex.org/W2842511635","https://openalex.org/W2887997457","https://openalex.org/W2914959431","https://openalex.org/W2944828972","https://openalex.org/W2956391471","https://openalex.org/W2963026686","https://openalex.org/W2963466845","https://openalex.org/W2963946669","https://openalex.org/W2964008076","https://openalex.org/W2964416181","https://openalex.org/W2964744899","https://openalex.org/W2982456085","https://openalex.org/W3005680577","https://openalex.org/W3006349040","https://openalex.org/W3009561768","https://openalex.org/W3016836174","https://openalex.org/W3033333779","https://openalex.org/W3033671339","https://openalex.org/W3034781633","https://openalex.org/W3035060554","https://openalex.org/W3035524453","https://openalex.org/W3036224891","https://openalex.org/W3092603779","https://openalex.org/W3093385868","https://openalex.org/W3102785203","https://openalex.org/W3108655343","https://openalex.org/W3116425452","https://openalex.org/W3120430728","https://openalex.org/W3129875423","https://openalex.org/W3132450979","https://openalex.org/W3134652006","https://openalex.org/W3166872541","https://openalex.org/W3171007011","https://openalex.org/W3196792593","https://openalex.org/W4286695273","https://openalex.org/W4287325132","https://openalex.org/W4288076043","https://openalex.org/W4297808394","https://openalex.org/W6677884823","https://openalex.org/W6682948231","https://openalex.org/W6730323794","https://openalex.org/W6734614811","https://openalex.org/W6734752668","https://openalex.org/W6735381419","https://openalex.org/W6735531217","https://openalex.org/W6735782011","https://openalex.org/W6754278344","https://openalex.org/W6762573206","https://openalex.org/W6766394743","https://openalex.org/W6774314701","https://openalex.org/W6774670964","https://openalex.org/W6778672394","https://openalex.org/W6779326418","https://openalex.org/W6779997284","https://openalex.org/W6786394082","https://openalex.org/W6791742336","https://openalex.org/W6795754764","https://openalex.org/W6796422976","https://openalex.org/W6929215198"],"related_works":["https://openalex.org/W2165912799","https://openalex.org/W2735662278","https://openalex.org/W3176438653","https://openalex.org/W2981628807","https://openalex.org/W4379875147","https://openalex.org/W3012393889","https://openalex.org/W3189091156","https://openalex.org/W4386087993","https://openalex.org/W4285815841","https://openalex.org/W3193641238"],"abstract_inverted_index":{"Self-supervised":[0],"representation":[1],"learning":[2,26,102,148,151],"has":[3],"been":[4],"extremely":[5,166],"successful":[6],"in":[7,60,153,195],"medical":[8],"image":[9,50,58,132],"analysis,":[10],"as":[11,36],"it":[12],"requires":[13],"no":[14],"human":[15],"annotations":[16],"to":[17,41,81,108,126],"provide":[18],"transferable":[19],"representations":[20,45],"for":[21,101],"downstream":[22],"tasks.":[23],"Recent":[24],"self-supervised":[25,103,150],"methods":[27],"are":[28],"dominated":[29],"by":[30,46,89,162,178],"noise-contrastive":[31],"estimation":[32],"(NCE,":[33],"also":[34],"known":[35],"contrastive":[37],"learning),":[38],"which":[39,97],"aims":[40],"learn":[42],"invariant":[43,85],"visual":[44],"contrasting":[47],"one":[48,70,75],"homogeneous":[49,76,110,131,141],"pair":[51,77],"with":[52,134,173],"a":[53,119,180],"large":[54],"number":[55],"of":[56,130,138],"heterogeneous":[57,115],"pairs":[59],"each":[61],"training":[62],"step.":[63],"Nonetheless,":[64],"NCE-based":[65],"approaches":[66],"still":[67],"suffer":[68],"from":[69,105],"major":[71],"problem":[72],"that":[73,189],"is":[74,78,98,122,202],"not":[79],"enough":[80],"extract":[82],"robust":[83],"and":[84,124,149,157],"semantic":[86],"information.":[87],"Inspired":[88],"the":[90,128,174,196],"archetypical":[91],"triplet":[92],"loss,":[93],"we":[94],"propose":[95],"GraVIS,":[96,179],"specifically":[99],"optimized":[100],"features":[104],"dermatology":[106,111],"images,":[107],"group":[109],"images":[112],"while":[113],"separating":[114],"ones.":[116,142],"In":[117],"addition,":[118],"hardness-aware":[120],"attention":[121],"introduced":[123],"incorporated":[125],"address":[127],"importance":[129],"views":[133],"similar":[135],"appearance":[136],"instead":[137],"those":[139],"dissimilar":[140],"GraVIS":[143],"significantly":[144],"outperforms":[145],"its":[146],"transfer":[147],"counterparts":[152],"both":[154],"lesion":[155],"segmentation":[156],"disease":[158],"classification":[159],"tasks,":[160],"sometimes":[161],"5":[163],"percents":[164],"under":[165],"limited":[167],"supervision.":[168],"More":[169],"importantly,":[170],"when":[171],"equipped":[172],"pre-trained":[175],"weights":[176],"provided":[177],"single":[181],"model":[182],"could":[183],"achieve":[184],"better":[185],"results":[186],"than":[187],"winners":[188],"heavily":[190],"rely":[191],"on":[192],"ensemble":[193],"strategies":[194],"well-known":[197],"ISIC":[198],"2017":[199],"challenge.":[200],"Code":[201],"available":[203],"at":[204],"https://bit.ly/3xiFyjx.":[205]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}