{"id":"https://openalex.org/W2999044405","doi":"https://doi.org/10.1109/tmi.2020.2966594","title":"Enabling a Single Deep Learning Model for Accurate Gland Instance Segmentation: A Shape-Aware Adversarial Learning Framework","display_name":"Enabling a Single Deep Learning Model for Accurate Gland Instance Segmentation: A Shape-Aware Adversarial Learning Framework","publication_year":2020,"publication_date":"2020-01-14","ids":{"openalex":"https://openalex.org/W2999044405","doi":"https://doi.org/10.1109/tmi.2020.2966594","mag":"2999044405","pmid":"https://pubmed.ncbi.nlm.nih.gov/31944936"},"language":"en","primary_location":{"id":"doi:10.1109/tmi.2020.2966594","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2020.2966594","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/A5079057344","display_name":"Zengqiang Yan","orcid":"https://orcid.org/0000-0002-2039-3863"},"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"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Zengqiang Yan","raw_affiliation_strings":["The Hong Kong University of Science and Technology, Hong Kong"],"affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology, Hong Kong","institution_ids":["https://openalex.org/I200769079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088751649","display_name":"Xin Yang","orcid":"https://orcid.org/0000-0001-6252-1061"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Yang","raw_affiliation_strings":["School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077687075","display_name":"Kwang\u2010Ting Cheng","orcid":"https://orcid.org/0000-0002-3885-4912"},"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"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Kwang-Ting Cheng","raw_affiliation_strings":["The Hong Kong University of Science and Technology, Hong Kong"],"affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology, Hong Kong","institution_ids":["https://openalex.org/I200769079"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5079057344"],"corresponding_institution_ids":["https://openalex.org/I200769079"],"apc_list":null,"apc_paid":null,"fwci":4.8007,"has_fulltext":false,"cited_by_count":51,"citation_normalized_percentile":{"value":0.95733057,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"39","issue":"6","first_page":"2176","last_page":"2189"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9987999796867371,"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":0.9987999796867371,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9936000108718872,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9904999732971191,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.7681435346603394},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6684597730636597},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6670315861701965},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6011568903923035},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.580318808555603},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5717843174934387},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5691697001457214},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5351553559303284},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5133196711540222},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4680083990097046},{"id":"https://openalex.org/keywords/active-shape-model","display_name":"Active shape model","score":0.4298028349876404},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.4248395562171936},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.41687315702438354},{"id":"https://openalex.org/keywords/similarity-measure","display_name":"Similarity measure","score":0.41670694947242737},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4157330095767975},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.41482013463974},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.307655930519104},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24025163054466248},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.20928451418876648}],"concepts":[{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.7681435346603394},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6684597730636597},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6670315861701965},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6011568903923035},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.580318808555603},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5717843174934387},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5691697001457214},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5351553559303284},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5133196711540222},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4680083990097046},{"id":"https://openalex.org/C129641003","wikidata":"https://www.wikidata.org/wiki/Q267189","display_name":"Active shape model","level":3,"score":0.4298028349876404},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4248395562171936},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.41687315702438354},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.41670694947242737},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4157330095767975},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.41482013463974},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.307655930519104},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24025163054466248},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.20928451418876648},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D006652","descriptor_name":"Histological Techniques","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006652","descriptor_name":"Histological Techniques","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006652","descriptor_name":"Histological Techniques","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":4,"locations":[{"id":"doi:10.1109/tmi.2020.2966594","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2020.2966594","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:31944936","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/31944936","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},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-104046","is_oa":false,"landing_page_url":"http://lbdiscover.ust.hk/uresolver?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rfr_id=info:sid/HKUST:SPI&rft.genre=article&rft.issn=0278-0062&rft.volume=v. 39&rft.issue=(6)&rft.date=2020&rft.spage=2176&rft.aulast=Yan&rft.aufirst=Z.&rft.atitle=Enabling+a+Single+Deep+Learning+Model+for+Accurate+Gland+Instance+Segmentation%3A+A+Shape-Aware+Adversarial+Learning+Framework&rft.title=IEEE+Transactions+on+Medical+Imaging","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"},{"id":"pmh:oai:repository.ust.hk:1783.1-104046","is_oa":false,"landing_page_url":"http://repository.ust.hk/ir/Record/1783.1-104046","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1830739424","display_name":null,"funder_award_id":"2019kfyRCPY118","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G4991219617","display_name":null,"funder_award_id":"61872417","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1731081199","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1923697677","https://openalex.org/W1948751323","https://openalex.org/W1950315773","https://openalex.org/W1985357898","https://openalex.org/W2092135626","https://openalex.org/W2100125385","https://openalex.org/W2159291411","https://openalex.org/W2216125271","https://openalex.org/W2304448845","https://openalex.org/W2482581235","https://openalex.org/W2516903654","https://openalex.org/W2550409828","https://openalex.org/W2562469482","https://openalex.org/W2577784528","https://openalex.org/W2593768305","https://openalex.org/W2618530766","https://openalex.org/W2625559849","https://openalex.org/W2632315370","https://openalex.org/W2750925197","https://openalex.org/W2752971446","https://openalex.org/W2753886374","https://openalex.org/W2767699072","https://openalex.org/W2805735218","https://openalex.org/W2885593519","https://openalex.org/W2890579034","https://openalex.org/W2892300271","https://openalex.org/W2921092847","https://openalex.org/W2963081269","https://openalex.org/W2963233928","https://openalex.org/W2963803174","https://openalex.org/W2964292098","https://openalex.org/W6637618735","https://openalex.org/W6640295612","https://openalex.org/W6673190769","https://openalex.org/W6674970907","https://openalex.org/W6683633756","https://openalex.org/W6743885473","https://openalex.org/W6745620410","https://openalex.org/W6753772092"],"related_works":["https://openalex.org/W3135697610","https://openalex.org/W2085033728","https://openalex.org/W2319693127","https://openalex.org/W308539617","https://openalex.org/W2072263576","https://openalex.org/W2474567666","https://openalex.org/W1940044583","https://openalex.org/W2806903871","https://openalex.org/W4320802053","https://openalex.org/W1517285738"],"abstract_inverted_index":{"Segmenting":[0],"gland":[1,25,51,87,208,267],"instances":[2,209],"in":[3,38,62,69,182,206,300],"histology":[4],"images":[5,185],"is":[6,151,171,204,305],"highly":[7],"challenging":[8],"as":[9],"it":[10,143,304],"requires":[11],"not":[12],"only":[13],"detecting":[14],"glands":[15],"from":[16],"a":[17,102,124,136,229],"complex":[18],"background":[19],"but":[20],"also":[21],"separating":[22],"each":[23,113],"individual":[24],"instance":[26,88,268],"with":[27,175,249,287],"accurate":[28,86],"boundary":[29,35,54,93,115,121,148,155,179,295],"detection.":[30,55,156],"However,":[31],"due":[32],"to":[33,78,107,147,173,193,223,308],"the":[34,70,92,109,118,129,160,163,166,215,243,250,254,273,281,294],"uncertainty":[36,94,149,296],"problem":[37,297],"manual":[39],"annotations,":[40],"pixel-to-pixel":[41,98],"matching":[42],"based":[43,241],"loss":[44,239,252],"functions":[45,240],"are":[46,189],"too":[47],"restrictive":[48],"for":[49,85,139,154,201,234,266],"simultaneous":[50],"detection":[52],"and":[53,67,117,150,191,197],"State-of-the-art":[56],"approaches":[57],"adopted":[58],"multi-model":[59],"schemes,":[60],"resulting":[61],"unnecessarily":[63],"high":[64],"model":[65,84,265],"complexity":[66],"difficulties":[68],"training":[71,219],"process.":[72],"In":[73],"this":[74],"paper,":[75],"we":[76,100,227],"propose":[77,101,228],"use":[79],"one":[80,261,288],"single":[81,262,289],"deep":[82,263,290],"learning":[83,258,264,291],"segmentation.":[89,269],"To":[90,213],"address":[91],"problem,":[95],"instead":[96],"of":[97,162,178,186,210,217],"matching,":[99],"segment-level":[103,130,167,244],"shape":[104,140,168,245],"similarity":[105,111,141,169,246],"measure":[106,131,170],"calculate":[108],"curve":[110],"between":[112],"annotated":[114],"segment":[116,122],"corresponding":[119],"detected":[120],"within":[123,135],"fixed":[125,137],"searching":[126,164],"range.":[127],"As":[128,293],"allows":[132],"location":[133],"variations":[134,216],"range":[138],"calculation,":[142],"has":[144],"better":[145],"tolerance":[146],"more":[152],"effective":[153],"Furthermore,":[157],"by":[158,221],"adjusting":[159],"radius":[161],"range,":[165],"able":[172],"deal":[174],"different":[176,187,211],"levels":[177],"uncertainty.":[180],"Therefore,":[181],"our":[183],"framework,":[184],"scales":[188],"down-sampled":[190],"integrated":[192],"provide":[194],"both":[195],"global":[196],"local":[198],"contextual":[199],"information":[200],"training,":[202],"which":[203],"helpful":[205],"segmenting":[207],"sizes.":[212],"reduce":[214],"multi-scale":[218],"images,":[220],"referring":[222],"adversarial":[224,251,257],"domain":[225,231],"adaptation,":[226],"pseudo":[230],"adaptation":[232],"framework":[233,259,283],"feature":[235],"alignment.":[236],"By":[237],"constructing":[238],"on":[242,272],"measure,":[247],"combining":[248],"function,":[253],"proposed":[255,282],"shape-aware":[256],"enables":[260],"Experimental":[270],"results":[271],"2015":[274],"MICCAI":[275],"Gland":[276],"Challenge":[277],"dataset":[278],"demonstrate":[279],"that":[280],"achieves":[284],"state-of-the-art":[285],"performance":[286],"model.":[292],"widely":[298],"exists":[299],"medical":[301],"image":[302],"segmentation,":[303],"broadly":[306],"applicable":[307],"other":[309],"applications.":[310]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":5}],"updated_date":"2026-03-12T08:34:05.389933","created_date":"2025-10-10T00:00:00"}