{"id":"https://openalex.org/W4308233758","doi":"https://doi.org/10.1109/icip46576.2022.9897927","title":"Semi-Supervised 3D Medical Image Segmentation Via Boundary-Aware Consistent Hidden Representation Learning","display_name":"Semi-Supervised 3D Medical Image Segmentation Via Boundary-Aware Consistent Hidden Representation Learning","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4308233758","doi":"https://doi.org/10.1109/icip46576.2022.9897927"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9897927","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897927","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","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/A5023302316","display_name":"Linhu Liu","orcid":"https://orcid.org/0000-0001-5253-6649"},"institutions":[{"id":"https://openalex.org/I4210156165","display_name":"Lenovo (China)","ror":"https://ror.org/04srd9d93","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210156165"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linhu Liu","raw_affiliation_strings":["Lenovo Research,Beijing,China","Lenovo Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lenovo Research,Beijing,China","institution_ids":["https://openalex.org/I4210156165"]},{"raw_affiliation_string":"Lenovo Research, Beijing, China","institution_ids":["https://openalex.org/I4210156165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101106695","display_name":"Jiang Tian","orcid":null},"institutions":[{"id":"https://openalex.org/I4210156165","display_name":"Lenovo (China)","ror":"https://ror.org/04srd9d93","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210156165"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiang Tian","raw_affiliation_strings":["Lenovo Research,Beijing,China","Lenovo Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lenovo Research,Beijing,China","institution_ids":["https://openalex.org/I4210156165"]},{"raw_affiliation_string":"Lenovo Research, Beijing, China","institution_ids":["https://openalex.org/I4210156165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101778565","display_name":"Xiangqian Cheng","orcid":"https://orcid.org/0000-0002-5598-8648"},"institutions":[{"id":"https://openalex.org/I4210156165","display_name":"Lenovo (China)","ror":"https://ror.org/04srd9d93","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210156165"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangqian Cheng","raw_affiliation_strings":["Lenovo Research,Beijing,China","Lenovo Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lenovo Research,Beijing,China","institution_ids":["https://openalex.org/I4210156165"]},{"raw_affiliation_string":"Lenovo Research, Beijing, China","institution_ids":["https://openalex.org/I4210156165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101873354","display_name":"Zhongchao Shi","orcid":"https://orcid.org/0000-0002-5216-3827"},"institutions":[{"id":"https://openalex.org/I4210156165","display_name":"Lenovo (China)","ror":"https://ror.org/04srd9d93","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210156165"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongchao Shi","raw_affiliation_strings":["Lenovo Research,Beijing,China","Lenovo Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lenovo Research,Beijing,China","institution_ids":["https://openalex.org/I4210156165"]},{"raw_affiliation_string":"Lenovo Research, Beijing, China","institution_ids":["https://openalex.org/I4210156165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100728569","display_name":"Jianping Fan","orcid":"https://orcid.org/0000-0003-2290-1785"},"institutions":[{"id":"https://openalex.org/I4210156165","display_name":"Lenovo (China)","ror":"https://ror.org/04srd9d93","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210156165"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianping Fan","raw_affiliation_strings":["Lenovo Research,Beijing,China","Lenovo Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lenovo Research,Beijing,China","institution_ids":["https://openalex.org/I4210156165"]},{"raw_affiliation_string":"Lenovo Research, Beijing, China","institution_ids":["https://openalex.org/I4210156165"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100728762","display_name":"Yong Rui","orcid":"https://orcid.org/0000-0002-9142-5914"},"institutions":[{"id":"https://openalex.org/I4210156165","display_name":"Lenovo (China)","ror":"https://ror.org/04srd9d93","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210156165"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Rui","raw_affiliation_strings":["Lenovo Research,Beijing,China","Lenovo Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lenovo Research,Beijing,China","institution_ids":["https://openalex.org/I4210156165"]},{"raw_affiliation_string":"Lenovo Research, Beijing, China","institution_ids":["https://openalex.org/I4210156165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210156165"],"apc_list":null,"apc_paid":null,"fwci":0.5548,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.75921963,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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":0.9998000264167786,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9994000196456909,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7035192251205444},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6973543763160706},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6529836654663086},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.627483606338501},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.6166921854019165},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5973713994026184},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5405430197715759},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.5294311046600342},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4838363230228424},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.46058720350265503},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.43851935863494873},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.4337535500526428},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.42090779542922974},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.29953962564468384},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18190351128578186}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7035192251205444},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6973543763160706},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6529836654663086},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.627483606338501},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.6166921854019165},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5973713994026184},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5405430197715759},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.5294311046600342},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4838363230228424},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.46058720350265503},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.43851935863494873},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4337535500526428},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.42090779542922974},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29953962564468384},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18190351128578186},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip46576.2022.9897927","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897927","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2012210378","https://openalex.org/W2464708700","https://openalex.org/W2839781211","https://openalex.org/W2889158831","https://openalex.org/W2953070460","https://openalex.org/W2962914239","https://openalex.org/W2979433110","https://openalex.org/W2979792663","https://openalex.org/W2979907638","https://openalex.org/W2996290406","https://openalex.org/W3035680157","https://openalex.org/W3044738063","https://openalex.org/W3083779147","https://openalex.org/W3090844657","https://openalex.org/W3095848620","https://openalex.org/W3097499499","https://openalex.org/W3102538446","https://openalex.org/W3171007011","https://openalex.org/W6733814495","https://openalex.org/W6754433467","https://openalex.org/W6764051988","https://openalex.org/W6776411772"],"related_works":["https://openalex.org/W3174759195","https://openalex.org/W3167013339","https://openalex.org/W4287121366","https://openalex.org/W3148060700","https://openalex.org/W3080681248","https://openalex.org/W4376646226","https://openalex.org/W60493759","https://openalex.org/W4287685660","https://openalex.org/W3047177827","https://openalex.org/W2057778272"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,69],"novel":[4],"Boundary-aware":[5],"Consistent":[6],"Hidden":[7],"Representation":[8],"Learning":[9],"Network":[10],"(BA-CHRLN),":[11],"which":[12,77],"contains":[13],"two":[14,28],"branches":[15,29],"for":[16],"semi-supervised":[17],"3D":[18],"medical":[19],"image":[20],"segmentation.":[21],"Inspired":[22],"by":[23,67],"the":[24,27,31,58,65,72,80,84,95,107,114,117],"contrastive":[25],"learning,":[26],"share":[30],"same":[32],"encoder":[33],"and":[34,43,83,102],"each":[35],"has":[36],"its":[37],"individual":[38],"decoder,":[39],"namely":[40],"supervised":[41],"decoder":[42],"unsupervised":[44],"one.":[45],"A":[46,87],"stop-gradient":[47],"operation":[48],"is":[49,90],"also":[50],"utilized":[51],"to":[52,93],"prevent":[53],"collapsing":[54],"of":[55,116],"solutions.":[56],"Taking":[57],"unlabeled":[59],"images":[60],"as":[61],"references,":[62],"BA-CHRLN":[63],"imposes":[64],"consistency":[66],"applying":[68],"perturbation":[70],"on":[71,106],"high-level":[73],"hidden":[74],"feature":[75],"representations,":[76],"significantly":[78],"improves":[79],"encoder\u2019s":[81],"representation":[82],"network\u2019s":[85],"robustness.":[86],"boundary-aware":[88],"map":[89],"further":[91],"introduced":[92],"capture":[94],"organ\u2019s":[96],"boundary":[97],"without":[98],"any":[99],"prior":[100],"knowledge":[101],"additional":[103],"parameters.":[104],"Experiments":[105],"Left":[108],"Atrium":[109],"(LA)":[110],"benchmark":[111],"dataset":[112],"demonstrate":[113],"effectiveness":[115],"BA-CHRLN.":[118]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}