{"id":"https://openalex.org/W2942739441","doi":"https://doi.org/10.1145/3314367.3314370","title":"Semantic Segmentation of Colon Gland with Conditional Generative Adversarial Network","display_name":"Semantic Segmentation of Colon Gland with Conditional Generative Adversarial Network","publication_year":2019,"publication_date":"2019-01-07","ids":{"openalex":"https://openalex.org/W2942739441","doi":"https://doi.org/10.1145/3314367.3314370","mag":"2942739441"},"language":"en","primary_location":{"id":"doi:10.1145/3314367.3314370","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3314367.3314370","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 9th International Conference on Bioscience, Biochemistry and Bioinformatics","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/A5022549905","display_name":"Liye Mei","orcid":"https://orcid.org/0000-0002-2555-9199"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liye Mei","raw_affiliation_strings":["Yunnan University, School of Information Science, and Engineering, Kunming, Yunnan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yunnan University, School of Information Science, and Engineering, Kunming, Yunnan, China","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014286651","display_name":"Xiaopeng Guo","orcid":"https://orcid.org/0000-0003-1111-2035"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaopeng Guo","raw_affiliation_strings":["Yunnan University, School of Information Science, and Engineering, Kunming, Yunnan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yunnan University, School of Information Science, and Engineering, Kunming, Yunnan, China","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051313233","display_name":"Chaowei Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaowei Cheng","raw_affiliation_strings":["Yunnan University, School of Information Science, and Engineering, Kunming, Yunnan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yunnan University, School of Information Science, and Engineering, Kunming, Yunnan, China","institution_ids":["https://openalex.org/I189210763"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I189210763"],"apc_list":null,"apc_paid":null,"fwci":0.1093,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.35694921,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"12","last_page":"16"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9947999715805054,"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":0.9947999715805054,"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/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.9912999868392944,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9902999997138977,"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/computer-science","display_name":"Computer science","score":0.7798658609390259},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7057836055755615},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.6540449857711792},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6254528164863586},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6035391688346863},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5512304902076721},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5140582323074341},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.4870362877845764},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4746021628379822},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4264529347419739},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3989311456680298},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.07698163390159607}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7798658609390259},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7057836055755615},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.6540449857711792},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6254528164863586},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6035391688346863},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5512304902076721},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5140582323074341},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.4870362877845764},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4746021628379822},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4264529347419739},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3989311456680298},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.07698163390159607},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3314367.3314370","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3314367.3314370","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 9th International Conference on Bioscience, Biochemistry and Bioinformatics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6899999976158142,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320324731","display_name":"Yunnan University","ror":"https://ror.org/0040axw97"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1901129140","https://openalex.org/W1950315773","https://openalex.org/W2011966237","https://openalex.org/W2099471712","https://openalex.org/W2125389028","https://openalex.org/W2128252595","https://openalex.org/W2134647348","https://openalex.org/W2169353679","https://openalex.org/W2194775991","https://openalex.org/W2288892845","https://openalex.org/W2511730936","https://openalex.org/W2552465644","https://openalex.org/W2580458810","https://openalex.org/W2618530766","https://openalex.org/W2748739903","https://openalex.org/W2896537220","https://openalex.org/W2899771611","https://openalex.org/W2963073614"],"related_works":["https://openalex.org/W4293320219","https://openalex.org/W2953246223","https://openalex.org/W3110074278","https://openalex.org/W4283584549","https://openalex.org/W2554314924","https://openalex.org/W4288256692","https://openalex.org/W2998859928","https://openalex.org/W3156863413","https://openalex.org/W4381885966","https://openalex.org/W2969399009"],"abstract_inverted_index":{"Semantic":[0],"segmentation":[1,170,200],"of":[2,18,68,117,121,188,204],"colon":[3,69,108,133],"gland":[4,70,107,122,134,181],"is":[5,25,100,126],"notoriously":[6],"challenging":[7,82],"due":[8],"to":[9,47,60,102,110,128,147,167,184],"their":[10],"complex":[11],"texture,":[12],"huge":[13],"variation,":[14],"and":[15,136,207],"the":[16,65,81,95,98,115,130,137,145,156,169,177,186,189],"scarcity":[17],"training":[19],"data":[20],"with":[21,212],"accurate":[22],"annotations.":[23],"It":[24],"even":[26],"hard":[27],"for":[28,64],"experts,":[29],"let":[30],"alone":[31],"computer-aided":[32],"diagnosis":[33],"systems.":[34],"Recently,":[35],"some":[36],"deep":[37],"convolutional":[38],"neural":[39],"networks":[40],"(DCNN)":[41],"based":[42],"methods":[43,57],"have":[44],"been":[45],"introduced":[46],"tackle":[48],"this":[49,77],"problem,":[50],"achieving":[51],"much":[52],"impressive":[53],"performance.":[54],"However,":[55],"these":[56],"always":[58],"tend":[59],"miss":[61],"segmented":[62],"results":[63],"important":[66],"regions":[67],"or":[71],"make":[72],"a":[73,86,104,111,119,160,164,198],"wrong":[74],"segmenting":[75],"decision.In":[76],"paper,":[78],"we":[79,154],"address":[80],"problem":[83],"by":[84],"proposed":[85,190],"novel":[87],"framework":[88,99],"through":[89],"conditional":[90],"generative":[91],"adversarial":[92,142],"network.":[93],"First,":[94],"generator":[96,146],"in":[97,202],"trained":[101],"learn":[103],"mapping":[105],"from":[106],"image":[109,135,162],"confidence":[112,138,151,157],"map":[113,158],"indicating":[114],"probabilities":[116],"being":[118],"pixel":[120],"object.":[123],"The":[124],"discriminator":[125],"responsible":[127],"penalize":[129],"mismatch":[131],"between":[132],"map.":[139,152],"This":[140],"additional":[141],"learning":[143],"facilitates":[144],"produce":[148],"higher":[149],"quality":[150],"Then":[153],"transform":[155],"into":[159],"binary":[161],"using":[163],"fixed":[165],"threshold":[166],"fulfill":[168],"task.":[171],"We":[172],"implement":[173],"extensive":[174],"experiments":[175],"on":[176],"public":[178],"benchmark":[179],"MICCAI":[180],"2015":[182],"dataset":[183],"verify":[185],"effectiveness":[187],"method.":[191],"Results":[192],"demonstrate":[193],"that":[194],"our":[195],"method":[196],"achieve":[197],"better":[199],"result":[201],"terms":[203],"visual":[205],"perception":[206],"two":[208],"quantitative":[209],"metrics,":[210],"compared":[211],"other":[213],"methods.":[214]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}