{"id":"https://openalex.org/W7126083972","doi":"https://doi.org/10.1109/bibm66473.2025.11357213","title":"Medical Image Segmentation Pruning via Feature Group Clustering with Spectral Variance Rebalancing","display_name":"Medical Image Segmentation Pruning via Feature Group Clustering with Spectral Variance Rebalancing","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126083972","doi":"https://doi.org/10.1109/bibm66473.2025.11357213"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11357213","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11357213","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/A5124258811","display_name":"Renda Han","orcid":null},"institutions":[{"id":"https://openalex.org/I20942203","display_name":"Hainan University","ror":"https://ror.org/03q648j11","country_code":"CN","type":"education","lineage":["https://openalex.org/I20942203"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Renda Han","raw_affiliation_strings":["School of Computer Science and Technology, Hainan University,Haikou,China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Hainan University,Haikou,China","institution_ids":["https://openalex.org/I20942203"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067275102","display_name":"Xuhao Guo","orcid":"https://orcid.org/0000-0002-2436-7961"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]},{"id":"https://openalex.org/I71270174","display_name":"Victoria University","ror":"https://ror.org/04j757h98","country_code":"AU","type":"education","lineage":["https://openalex.org/I71270174"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xuhao Guo","raw_affiliation_strings":["University of Melbourne,Faculty of Engineering and Information Technology,Victoria,Australia"],"affiliations":[{"raw_affiliation_string":"University of Melbourne,Faculty of Engineering and Information Technology,Victoria,Australia","institution_ids":["https://openalex.org/I71270174","https://openalex.org/I165779595"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063163113","display_name":"Yihao Chen","orcid":"https://orcid.org/0009-0002-9703-7645"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yihao Chen","raw_affiliation_strings":["College of Control Science and Engineering, Zhejiang University,Hangzhou,China"],"affiliations":[{"raw_affiliation_string":"College of Control Science and Engineering, Zhejiang University,Hangzhou,China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5124258811"],"corresponding_institution_ids":["https://openalex.org/I20942203"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.7243489,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"927","last_page":"932"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.4472000002861023,"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":0.4472000002861023,"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":0.10899999737739563,"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":0.060499999672174454,"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/redundancy","display_name":"Redundancy (engineering)","score":0.6348999738693237},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6244999766349792},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6230000257492065},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6075000166893005},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.541700005531311},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5081999897956848},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49239999055862427},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.43549999594688416},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.4074000120162964}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7013999819755554},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6651999950408936},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.6348999738693237},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6244999766349792},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6230000257492065},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6075000166893005},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.541700005531311},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5081999897956848},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49239999055862427},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.43549999594688416},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.4074000120162964},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3871999979019165},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.3840000033378601},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.35010001063346863},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3330000042915344},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.3305000066757202},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3181999921798706},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3021000027656555},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.30160000920295715},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.29789999127388},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.29420000314712524},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.28459998965263367},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.2791000008583069},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.2784000039100647},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2676999866962433},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2547999918460846}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11357213","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11357213","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":[{"score":0.4265514314174652,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1909740415","https://openalex.org/W1994616650","https://openalex.org/W2104276184","https://openalex.org/W2171180835","https://openalex.org/W2798603777","https://openalex.org/W3034513523","https://openalex.org/W3202323235","https://openalex.org/W4221163766","https://openalex.org/W4415707998","https://openalex.org/W6902762063"],"related_works":[],"abstract_inverted_index":{"Medical":[0],"image":[1,42],"segmentation":[2,43,72,223],"models":[3,138,196],"have":[4],"play":[5],"a":[6,36,112],"significant":[7],"and":[8,17,128,151,161,202,211,225],"essential":[9],"role":[10],"in":[11,23,79,85,221],"modern":[12],"healthcare,":[13],"while":[14,52,139,168],"excessive":[15],"computational":[16],"storage":[18],"resources":[19],"limit":[20],"their":[21,54,141,156],"deployment":[22],"real-world":[24],"medical":[25,41],"scenarios.":[26],"To":[27],"address":[28],"these":[29,89,120],"challenges,":[30],"structured":[31,59,114],"pruning":[32,60,96,115],"has":[33],"emerged":[34],"as":[35,75,158],"promising":[37],"solution":[38],"for":[39,46],"compressing":[40],"models,":[44,73],"allowing":[45],"the":[47,67,76,82,93,98,147,175,179],"removal":[48],"of":[49,71,95,137,149,178],"entire":[50],"components":[51,157],"preserving":[53,169],"regular":[55],"structure.":[56],"Nevertheless,":[57],"existing":[58,219],"methods":[61,90],"often":[62,91],"fail":[63],"to":[64,103,133,165],"fully":[65],"exploit":[66],"unique":[68],"architectural":[69],"characteristics":[70],"such":[74],"encoder-decoder":[77],"symmetry":[78],"CNNs":[80],"or":[81],"residual":[83],"connections":[84],"Transformers.":[86],"In":[87,107],"addition,":[88],"neglect":[92],"impact":[94],"on":[97,188,194],"model's":[99],"activation":[100],"statistics,":[101],"leading":[102],"potential":[104],"performance":[105,224],"degradation.":[106],"this":[108],"paper,":[109],"we":[110],"propose":[111],"novel":[113],"method":[116,217],"that":[117,215],"specifically":[118],"targets":[119],"limitations":[121],"by":[122,154,182],"introducing":[123],"Feature":[124],"Group":[125],"Clustering":[126,164],"(FGC)":[127],"Spectral":[129,163],"Variance":[130],"Rebalancing":[131],"(SVR)":[132],"effectively":[134],"reduce":[135],"redundancy":[136,167],"maintaining":[140],"statistical":[142,176],"properties.":[143],"Specifically,":[144],"FGC":[145],"unifies":[146],"handling":[148],"CNN-based":[150],"Transformer-based":[152],"architectures":[153],"treating":[155],"feature":[159],"groups,":[160],"employing":[162],"eliminate":[166],"critical":[170],"structures.":[171],"Furthermore,":[172],"SVR":[173],"restores":[174],"properties":[177],"pruned":[180],"model":[181],"analytically":[183],"rescaling":[184],"normalization":[185],"parameters":[186],"based":[187],"intra-cluster":[189],"spectral":[190],"cor-relations.":[191],"Extensive":[192],"experiments":[193],"multiple":[195],"including":[197],"3D":[198],"TransUNet,":[199],"Swin":[200],"UNETR,":[201],"MedFormer":[203],"across":[204],"Synapse":[205],"Multi-organ":[206],"CT,":[207],"BraTS2020,":[208],"Abdomen":[209,212],"CT":[210],"MRI":[213],"demonstrate":[214],"our":[216],"outperforms":[218],"techniques":[220],"both":[222],"compression":[226],"effects.":[227]},"counts_by_year":[],"updated_date":"2026-02-01T03:34:12.195049","created_date":"2026-01-30T00:00:00"}