{"id":"https://openalex.org/W4390493616","doi":"https://doi.org/10.1109/cisp-bmei60920.2023.10373337","title":"MIPR: Automatic Segmentation Annotation of Medical Images via Generative model","display_name":"MIPR: Automatic Segmentation Annotation of Medical Images via Generative model","publication_year":2023,"publication_date":"2023-10-28","ids":{"openalex":"https://openalex.org/W4390493616","doi":"https://doi.org/10.1109/cisp-bmei60920.2023.10373337"},"language":"en","primary_location":{"id":"doi:10.1109/cisp-bmei60920.2023.10373337","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/cisp-bmei60920.2023.10373337","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"},"type":"article","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/A5109313946","display_name":"Haiming Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]},{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haiming Zhu","raw_affiliation_strings":["Tsinghua University,Tsinghua Shenzhen International Graduate School,Shenzhen,China","Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,Tsinghua Shenzhen International Graduate School,Shenzhen,China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]},{"raw_affiliation_string":"Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102893845","display_name":"Pingping Dai","orcid":"https://orcid.org/0000-0002-4230-7487"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pingping Dai","raw_affiliation_strings":["Tsinghua University,Tsinghua Shenzhen International Graduate School,Shenzhen,China","Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,Tsinghua Shenzhen International Graduate School,Shenzhen,China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]},{"raw_affiliation_string":"Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100743772","display_name":"Hao Wang","orcid":"https://orcid.org/0000-0002-0536-5039"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Wang","raw_affiliation_strings":["Tsinghua University,Tsinghua Shenzhen International Graduate School,Shenzhen,China","Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,Tsinghua Shenzhen International Graduate School,Shenzhen,China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]},{"raw_affiliation_string":"Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091832443","display_name":"Xiang Qian","orcid":"https://orcid.org/0000-0003-2487-8785"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]},{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Qian","raw_affiliation_strings":["Tsinghua University,Tsinghua Shenzhen International Graduate School,Shenzhen,China","Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,Tsinghua Shenzhen International Graduate School,Shenzhen,China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]},{"raw_affiliation_string":"Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5109313946"],"corresponding_institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18293281,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9987999796867371,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9987999796867371,"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/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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9980000257492065,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8173379898071289},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.8007849454879761},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7049799561500549},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.661285936832428},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5167957544326782},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5071804523468018},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5003080368041992},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4442318081855774},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4375007748603821},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43213874101638794},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.43161624670028687},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4284954071044922},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.41214701533317566}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8173379898071289},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.8007849454879761},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7049799561500549},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.661285936832428},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5167957544326782},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5071804523468018},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5003080368041992},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4442318081855774},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4375007748603821},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43213874101638794},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.43161624670028687},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4284954071044922},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.41214701533317566},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cisp-bmei60920.2023.10373337","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/cisp-bmei60920.2023.10373337","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.6600000262260437}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1641498739","https://openalex.org/W1861492603","https://openalex.org/W1901129140","https://openalex.org/W2133665775","https://openalex.org/W2288892845","https://openalex.org/W2395611524","https://openalex.org/W2507296351","https://openalex.org/W2613456556","https://openalex.org/W2630837129","https://openalex.org/W2745006834","https://openalex.org/W2884436604","https://openalex.org/W2884561390","https://openalex.org/W2891179298","https://openalex.org/W2958187724","https://openalex.org/W2962974533","https://openalex.org/W2963043051","https://openalex.org/W2963800363","https://openalex.org/W2963946669","https://openalex.org/W2997286550","https://openalex.org/W3014974815","https://openalex.org/W3028210663","https://openalex.org/W3035127651","https://openalex.org/W3090605478","https://openalex.org/W3109889023","https://openalex.org/W3112701542","https://openalex.org/W3126915734","https://openalex.org/W3127751679","https://openalex.org/W3133459377","https://openalex.org/W3161307971","https://openalex.org/W3203886626","https://openalex.org/W4200284523","https://openalex.org/W4225343834","https://openalex.org/W4298165656","https://openalex.org/W6640963894","https://openalex.org/W6750469568","https://openalex.org/W6797179183"],"related_works":["https://openalex.org/W4365211920","https://openalex.org/W3014948380","https://openalex.org/W4380551139","https://openalex.org/W4317695495","https://openalex.org/W4287117424","https://openalex.org/W4387506531","https://openalex.org/W4238433571","https://openalex.org/W2967848559","https://openalex.org/W4299831724","https://openalex.org/W4283803360"],"abstract_inverted_index":{"Most":[0],"state-of-the-art":[1],"semantic":[2],"segmentation":[3,174,190],"methods":[4],"in":[5,78],"the":[6,44,63,88,105,122,127,130,137,156,179,184,200],"medical":[7,68,95,141,206],"domain":[8,24],"rely":[9],"on":[10,178],"fully":[11],"supervised":[12],"deep":[13],"learning.":[14],"However,":[15],"creating":[16],"high-quality":[17],"annotated":[18,47,92],"datasets":[19],"demands":[20],"extensive":[21],"labor":[22],"and":[23,30,39,53,118,129,171],"expertise,":[25],"leading":[26],"to":[27,42,86,108,148],"significant":[28],"time":[29],"cost":[31],"investments.":[32],"While":[33],"previous":[34],"approaches":[35],"have":[36],"explored":[37],"semi-supervised":[38,79],"unsupervised":[40],"learning":[41],"alleviate":[43],"scarcity":[45],"of":[46,61,90,152,158,202],"data":[48,153],"by":[49,67,140,188,205],"leveraging":[50],"unlabeled":[51,159],"samples":[52],"achieving":[54],"promising":[55],"results,":[56],"they":[57],"still":[58],"fall":[59],"short":[60],"replicating":[62],"nuanced":[64],"annotations":[65,132,138,203],"provided":[66,204],"professionals.":[69],"In":[70],"this":[71],"paper,":[72],"drawing":[73],"inspiration":[74],"from":[75,155],"self-training":[76],"techniques":[77],"learning,":[80],"we":[81],"propose":[82],"an":[83],"innovative":[84],"approach":[85],"address":[87],"issue":[89],"limited":[91],"data,":[93],"called":[94],"images":[96,124],"pixel":[97],"rearrangement.":[98],"Our":[99,113],"method":[100,114,191],"combines":[101],"image":[102,111],"editing":[103],"with":[104,119,136],"pseudo-label":[106],"methodology":[107,162],"generate":[109],"labeled":[110,150],"pairs.":[112],"supports":[115],"iterative":[116],"training,":[117],"each":[120],"iteration,":[121],"edited":[123],"progressively":[125],"resemble":[126],"originals,":[128],"resulting":[131],"align":[133],"more":[134],"closely":[135],"made":[139],"experts.":[142,207],"This":[143],"novel":[144],"process":[145],"allows":[146],"us":[147],"derive":[149],"pairs":[151],"directly":[154],"pool":[157],"data.":[160],"The":[161],"is":[163,192],"implemented":[164],"through":[165],"a":[166,172],"purpose-built":[167],"conditional":[168],"generative":[169],"model":[170],"dedicated":[173],"network.":[175],"Experimental":[176],"results":[177],"ISIC18":[180],"dataset":[181],"demonstrate":[182],"that":[183],"annotation":[185],"quality":[186,201],"achieved":[187],"our":[189],"not":[193],"only":[194],"comparable":[195],"to,":[196],"but":[197],"potentially":[198],"surpasses":[199]},"counts_by_year":[],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}
