{"id":"https://openalex.org/W7126088952","doi":"https://doi.org/10.1109/bibm66473.2025.11356748","title":"Incremental Segmentation Method for Cardiac Tissue Categories Based on Class-Aware Contrastive Learning","display_name":"Incremental Segmentation Method for Cardiac Tissue Categories Based on Class-Aware Contrastive Learning","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126088952","doi":"https://doi.org/10.1109/bibm66473.2025.11356748"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356748","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356748","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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/A5124188567","display_name":"Wen Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wen Lu","raw_affiliation_strings":["Northeastern University,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"Northeastern University,Shenyang,China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062056634","display_name":"Shuaizheng Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuaizheng Chen","raw_affiliation_strings":["Northeastern University,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"Northeastern University,Shenyang,China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034810639","display_name":"Chaolu Feng","orcid":"https://orcid.org/0000-0002-5575-2328"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaolu Feng","raw_affiliation_strings":["Northeastern University,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"Northeastern University,Shenyang,China","institution_ids":["https://openalex.org/I9224756"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5124188567"],"corresponding_institution_ids":["https://openalex.org/I9224756"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.72451952,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7332","last_page":"7339"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.3481000065803528,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.3481000065803528,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.26080000400543213,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.057999998331069946,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6572999954223633},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6211000084877014},{"id":"https://openalex.org/keywords/forgetting","display_name":"Forgetting","score":0.6197999715805054},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5874000191688538},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.4902999997138977},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4805999994277954},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4645000100135803}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6848999857902527},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6572999954223633},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6330999732017517},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6211000084877014},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.6197999715805054},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5874000191688538},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4902999997138977},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4805999994277954},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4645000100135803},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43130001425743103},{"id":"https://openalex.org/C2777220311","wikidata":"https://www.wikidata.org/wiki/Q6423340","display_name":"Knowledge acquisition","level":2,"score":0.3422999978065491},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32510000467300415},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.31709998846054077},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2858999967575073},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C2777629044","wikidata":"https://www.wikidata.org/wiki/Q614959","display_name":"Contrastive analysis","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.2637999951839447}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356748","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356748","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.6134405136108398}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1983577042","https://openalex.org/W2060277733","https://openalex.org/W2116226630","https://openalex.org/W2560647685","https://openalex.org/W2804047627","https://openalex.org/W2962914239","https://openalex.org/W2964189064","https://openalex.org/W3000677656","https://openalex.org/W3007041883","https://openalex.org/W3021931813","https://openalex.org/W3030364939","https://openalex.org/W3034435444","https://openalex.org/W3035524453","https://openalex.org/W3074741277","https://openalex.org/W3107810305","https://openalex.org/W3171888599","https://openalex.org/W3172614365","https://openalex.org/W4312308876","https://openalex.org/W4312309344","https://openalex.org/W4387250144","https://openalex.org/W4389164266","https://openalex.org/W4402082562","https://openalex.org/W7117454567"],"related_works":[],"abstract_inverted_index":{"Deep":[0],"learning":[1,45,61,89,102,183,288],"methods":[2,17],"have":[3,18,36],"become":[4],"a":[5,59,87,166,181,275],"staple":[6],"in":[7,74,105],"the":[8,25,64,81,93,98,116,121,124,134,144,178,189,192,196,201,223,238,243,253,264,269,279,287],"field":[9],"of":[10,27,67,80,95,100,103,118,126,136,146,191,198,206,218,225,240,263,281,290],"medical":[11,28,68],"image":[12,29],"segmentation.":[13],"While":[14],"traditional":[15],"segmentation":[16,111],"achieved":[19],"significant":[20,276],"success,":[21],"challenges":[22],"such":[23],"as":[24],"labelling":[26],"datasets":[30,234],"and":[31,208,246],"concerns":[32],"regarding":[33],"patient":[34],"privacy":[35],"constrained":[37],"their":[38,75],"practical":[39],"application.":[40],"In":[41,78],"recent":[42],"years,":[43],"incremental":[44,101],"has":[46,161,274],"garnered":[47],"considerable":[48],"attention":[49],"due":[50],"to":[51,54,132,157,172],"its":[52],"ability":[53,289],"address":[55],"these":[56],"limitations":[57],"through":[58],"step-by-step":[60],"approach.":[62],"Nevertheless,":[63],"inextricable":[65],"nature":[66],"images":[69],"between":[70,242],"organisations":[71],"can":[72],"result":[73],"catastrophic":[76],"oblivion.":[77],"light":[79],"aforementioned":[82],"reasons,":[83],"this":[84],"study":[85],"proposes":[86],"deep":[88],"framework,":[90],"CCL,":[91],"with":[92],"objective":[94],"systematically":[96],"addressing":[97],"challenge":[99],"categories":[104],"cardiac":[106,231],"magnetic":[107,232],"resonance":[108,233],"imaging":[109],"(CMR)":[110],"tasks.":[112],"Rather":[113],"than":[114],"employing":[115],"reutilisation":[117,135,145],"pre-existing":[119],"images,":[120],"method":[122,255],"deploys":[123],"utilisation":[125],"pseudo-labels":[127,152],"generated":[128,153],"from":[129,177],"legacy":[130],"models":[131,156],"facilitate":[133],"acquired":[137],"knowledge.":[138,227],"This":[139,214],"approach":[140],"does":[141],"not":[142],"involve":[143],"preexisting":[147],"images;":[148],"rather,":[149],"it":[150],"employs":[151],"by":[154,211],"preceding":[155],"reclaim":[158],"knowledge":[159,220],"that":[160,252,268],"been":[162],"previously":[163],"acquired.":[164],"Firstly,":[165],"confidence-based":[167],"pseudolabel":[168],"strategy":[169],"is":[170,185,295],"employed":[171],"retrieve":[173],"old":[174,282],"category":[175],"pixels":[176],"background.":[179],"Secondly,":[180],"contrastive":[182],"mechanism":[184,273],"proposed,":[186],"based":[187],"on":[188,278],"characteristics":[190],"teacher-student":[193],"model.":[194],"During":[195],"training":[197],"new":[199,226,291],"models,":[200],"teacher":[202],"model":[203],"corrects":[204],"errors":[205],"forgetting":[207,280],"consistency":[209],"caused":[210],"incorrect":[212],"pseudolabels.":[213],"ensures":[215],"maximum":[216],"retention":[217],"prior":[219],"without":[221,285],"compromising":[222],"acquisition":[224],"Two":[228],"publicly":[229],"available":[230,296],"were":[235],"utilised":[236],"for":[237],"purpose":[239],"comparison":[241],"proposed":[244,270],"methodology":[245],"classical":[247],"approaches.":[248],"The":[249,261,293],"findings":[250,262],"demonstrated":[251],"CCL":[254],"outperformed":[256],"several":[257],"other":[258],"excellent":[259],"methods.":[260],"quantitative":[265],"analysis":[266],"suggest":[267],"feature":[271],"contrast":[272],"effect":[277],"class":[283],"features,":[284],"affecting":[286],"classes.":[292],"code":[294],"at":[297],"https://anonymous.4open.science/r/ccl-3C56/.":[298]},"counts_by_year":[],"updated_date":"2026-02-01T03:34:12.195049","created_date":"2026-01-30T00:00:00"}
