{"id":"https://openalex.org/W4415540972","doi":"https://doi.org/10.1145/3746027.3755652","title":"Simple but Effective: Sub-Volume Contrastive Learning for Class-Imbalanced Semi-Supervised 3D Medical Image Segmentation","display_name":"Simple but Effective: Sub-Volume Contrastive Learning for Class-Imbalanced Semi-Supervised 3D Medical Image Segmentation","publication_year":2025,"publication_date":"2025-10-25","ids":{"openalex":"https://openalex.org/W4415540972","doi":"https://doi.org/10.1145/3746027.3755652"},"language":null,"primary_location":{"id":"doi:10.1145/3746027.3755652","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755652","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","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":null,"display_name":"Xianrun Xu","orcid":"https://orcid.org/0009-0007-4553-9527"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xianrun Xu","raw_affiliation_strings":["Guangdong University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangdong University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015849144","display_name":"Baoyao Yang","orcid":"https://orcid.org/0000-0001-9092-3164"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baoyao Yang","raw_affiliation_strings":["School of Computers, Guangdong University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computers, Guangdong University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043818900","display_name":"Wanyun Li","orcid":"https://orcid.org/0000-0001-9224-1618"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanyun Li","raw_affiliation_strings":["Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jingsong Lin","orcid":"https://orcid.org/0009-0004-6322-982X"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingsong Lin","raw_affiliation_strings":["Guangdong University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangdong University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yufei Xu","orcid":"https://orcid.org/0009-0005-2764-2876"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yufei Xu","raw_affiliation_strings":["Guangdong University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangdong University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139024713"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1603674,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4884","last_page":"4893"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9994000196456909,"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":0.9994000196456909,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9975000023841858,"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"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9973000288009644,"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/segmentation","display_name":"Segmentation","score":0.7056000232696533},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6588000059127808},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.555899977684021},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.506600022315979},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.49619999527931213},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.45410001277923584},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.4490000009536743},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.44699999690055847}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.760699987411499},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7293999791145325},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7056000232696533},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6588000059127808},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.555899977684021},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.506600022315979},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.49619999527931213},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.45410001277923584},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4490000009536743},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.44699999690055847},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.39910000562667847},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.39899998903274536},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.39640000462532043},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.375},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3596999943256378},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3440999984741211},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.34209999442100525},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2727999985218048},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.251800000667572},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746027.3755652","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755652","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4105872759","display_name":null,"funder_award_id":"2025A1515011385;2024A1515010186","funder_id":"https://openalex.org/F4320321921","funder_display_name":"Natural Science Foundation of Guangdong Province"},{"id":"https://openalex.org/G7259373857","display_name":null,"funder_award_id":"62472105","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/F4320321921","display_name":"Natural Science Foundation of Guangdong Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2962804657","https://openalex.org/W2962933664","https://openalex.org/W2975885948","https://openalex.org/W3157653192","https://openalex.org/W3167251133","https://openalex.org/W3168822201","https://openalex.org/W3171581326","https://openalex.org/W4280631251","https://openalex.org/W4313291198","https://openalex.org/W4313478989","https://openalex.org/W4384206867","https://openalex.org/W4385880767","https://openalex.org/W4386233974","https://openalex.org/W4386609092","https://openalex.org/W4387159337","https://openalex.org/W4387415409","https://openalex.org/W4403791961"],"related_works":[],"abstract_inverted_index":{"Medical":[0],"image":[1,89],"segmentation":[2,146],"is":[3],"essential":[4],"for":[5,27,148],"precise":[6],"anatomical":[7],"delineation":[8],"and":[9,61],"clinical":[10],"decision-making.":[11],"However,":[12],"fully":[13],"supervised":[14],"methods":[15],"are":[16],"limited":[17],"by":[18,37,45,134],"the":[19,54],"substantial":[20,152],"cost":[21],"of":[22],"acquiring":[23],"pixel-level":[24],"annotations,":[25],"particularly":[26],"3D":[28,87],"volumetric":[29],"data.":[30],"Semi-supervised":[31],"learning":[32,96],"(SSL)":[33],"alleviates":[34],"this":[35,66],"challenge":[36],"leveraging":[38],"unlabeled":[39],"data,":[40],"yet":[41],"it":[42],"remains":[43],"hindered":[44],"severe":[46],"class":[47],"imbalance,":[48],"where":[49],"dominant":[50],"structures":[51],"disproportionately":[52],"occupy":[53],"voxel":[55],"space,":[56],"leading":[57],"to":[58,81,107],"feature":[59,83,128],"degradation":[60],"unreliable":[62],"pseudo-labels.":[63],"To":[64],"address":[65],"issue,":[67],"we":[68,117],"propose":[69],"a":[70,119],"simple":[71],"but":[72,103],"effective":[73],"SSL":[74],"framework,":[75],"namely":[76],"Sub-Volume":[77],"Contrastive":[78],"Learning":[79],"(SuVCL),":[80],"enhance":[82],"discriminability":[84],"in":[85],"imbalanced":[86],"medical":[88],"segmentation.":[90],"Our":[91],"approach":[92],"incorporates":[93],"localized":[94],"contrastive":[95],"through":[97],"sub-volume":[98],"sampling,":[99],"which":[100,124],"captures":[101],"small":[102],"semantically":[104],"informative":[105],"regions":[106],"retain":[108],"fine-grained":[109],"structural":[110],"details":[111],"while":[112],"mitigating":[113],"computational":[114],"overhead.":[115],"Furthermore,":[116],"introduce":[118],"balanced":[120],"memory":[121],"bank":[122],"mechanism,":[123],"dynamically":[125],"maintains":[126],"class-specific":[127],"representations":[129],"with":[130],"adaptive":[131],"updates":[132],"guided":[133],"class-predictive":[135],"confidence.":[136],"Extensive":[137],"experimental":[138],"evaluations":[139],"demonstrate":[140],"that":[141],"our":[142],"method":[143],"substantially":[144],"enhances":[145],"performance":[147,153],"minority":[149],"classes,":[150],"demonstrating":[151],"gains":[154],"over":[155],"existing":[156],"SOTAs.":[157]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-25T00:00:00"}
