{"id":"https://openalex.org/W7126039092","doi":"https://doi.org/10.1109/bibm66473.2025.11356511","title":"A Novel Graph-Based Skip Connection Design for Hierarchical and Relational Feature Modeling","display_name":"A Novel Graph-Based Skip Connection Design for Hierarchical and Relational Feature Modeling","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126039092","doi":"https://doi.org/10.1109/bibm66473.2025.11356511"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356511","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356511","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/A5124158734","display_name":"Wei Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I56934997","display_name":"Changsha University of Science and Technology","ror":"https://ror.org/03yph8055","country_code":"CN","type":"education","lineage":["https://openalex.org/I56934997"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wei Wang","raw_affiliation_strings":["School of Computer Science and Technology, Changsha University of Science and Technology,China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Changsha University of Science and Technology,China","institution_ids":["https://openalex.org/I56934997"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053183644","display_name":"Tao Yang","orcid":"https://orcid.org/0000-0002-4082-5264"},"institutions":[{"id":"https://openalex.org/I56934997","display_name":"Changsha University of Science and Technology","ror":"https://ror.org/03yph8055","country_code":"CN","type":"education","lineage":["https://openalex.org/I56934997"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Yang","raw_affiliation_strings":["School of Computer Science and Technology, Changsha University of Science and Technology,China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Changsha University of Science and Technology,China","institution_ids":["https://openalex.org/I56934997"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124239010","display_name":"Xin Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I56934997","display_name":"Changsha University of Science and Technology","ror":"https://ror.org/03yph8055","country_code":"CN","type":"education","lineage":["https://openalex.org/I56934997"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Wang","raw_affiliation_strings":["School of Computer Science and Technology, Changsha University of Science and Technology,China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Changsha University of Science and Technology,China","institution_ids":["https://openalex.org/I56934997"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5124158734"],"corresponding_institution_ids":["https://openalex.org/I56934997"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.72264268,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1845","last_page":"1849"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.6010000109672546,"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":0.6010000109672546,"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":0.05050000175833702,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.04910000041127205,"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/convolution","display_name":"Convolution (computer science)","score":0.5708000063896179},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5457000136375427},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5238000154495239},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5169000029563904},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5101000070571899},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4950000047683716},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4287000000476837},{"id":"https://openalex.org/keywords/dice","display_name":"Dice","score":0.42250001430511475},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4077000021934509},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.38760000467300415}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7817000150680542},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5708000063896179},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5457000136375427},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5238000154495239},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5169000029563904},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5101000070571899},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5001000165939331},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4950000047683716},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46209999918937683},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4287000000476837},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4259999990463257},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.42250001430511475},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4077000021934509},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.38760000467300415},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3700000047683716},{"id":"https://openalex.org/C27511587","wikidata":"https://www.wikidata.org/wiki/Q2178623","display_name":"Spatial relation","level":2,"score":0.3400000035762787},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.33730000257492065},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.33079999685287476},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.3160000145435333},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C13355873","wikidata":"https://www.wikidata.org/wiki/Q2920850","display_name":"Connection (principal bundle)","level":2,"score":0.2962000072002411},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C40207289","wikidata":"https://www.wikidata.org/wiki/Q755662","display_name":"Relational model","level":3,"score":0.2838999927043915},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.28189998865127563},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C143271835","wikidata":"https://www.wikidata.org/wiki/Q254515","display_name":"Similitude","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.25859999656677246}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356511","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356511","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2963946669","https://openalex.org/W2979750740","https://openalex.org/W2990045899","https://openalex.org/W3138516171","https://openalex.org/W3175515048","https://openalex.org/W4296425595","https://openalex.org/W4312568229","https://openalex.org/W4312847199","https://openalex.org/W4321232185","https://openalex.org/W4385541821","https://openalex.org/W4387430177","https://openalex.org/W4391235287","https://openalex.org/W4394593199","https://openalex.org/W4394596865","https://openalex.org/W4402903991","https://openalex.org/W4406260521","https://openalex.org/W4414373498"],"related_works":[],"abstract_inverted_index":{"Accurately":[0],"extracting":[1],"both":[2,159],"global":[3],"context":[4],"and":[5,28,38,80,94,139,143,155,161,178],"detailed":[6],"local":[7],"features":[8,93],"remains":[9],"a":[10,49,130],"significant":[11],"hurdle":[12],"in":[13,181],"medical":[14,182],"image":[15,183],"segmentation.":[16,184],"Traditional":[17],"techniques":[18],"often":[19],"lack":[20],"the":[21,59,66,95,104,116,123,147,153,167,188],"capacity":[22],"to":[23,35,88,113,176],"effectively":[24],"model":[25,148],"hierarchical":[26],"structures":[27],"pixel-level":[29],"spatial":[30,92,108],"dependencies,":[31],"which":[32,57],"can":[33,186],"lead":[34],"blurred":[36],"boundaries":[37],"loss":[39],"of":[40,118,135,169],"intricate":[41],"details.":[42],"To":[43],"overcome":[44],"these":[45],"limitations,":[46],"we":[47],"introduce":[48],"novel":[50],"graph-based":[51,170],"skip":[52,86],"connection":[53],"design":[54],"named":[55],"SGraphNet,":[56],"combines":[58],"Parallel":[60],"Hierarchical-Relational":[61],"Graph":[62],"(PHRG)":[63],"module":[64,74,106],"with":[65,110],"Dynamic":[67],"Attention":[68],"Convolution":[69],"(DAC)":[70],"module.":[71],"The":[72],"PHRG":[73],"builds":[75],"Hierarchical":[76],"Spatial":[77],"Graphs":[78,83],"(HSG)":[79],"Relational":[81],"Position":[82],"(RPG)":[84],"as":[85,173],"connections":[87],"independently":[89],"capture":[90],"multi-scale":[91],"relative":[96],"dependencies":[97],"among":[98],"different":[99],"feature":[100],"regions.":[101],"Complementing":[102],"this,":[103],"DAC":[105],"integrates":[107],"attention":[109],"dynamic":[111],"convolution":[112],"further":[114],"enhance":[115],"representation":[117],"features.":[119],"Experiments":[120],"conducted":[121],"on":[122,152,190],"Synapse":[124],"dataset":[125],"show":[126],"that":[127],"SGraphNet":[128],"achieves":[129],"Dice":[131],"Similarity":[132],"Coefficient":[133],"(DSC)":[134],"84.88%,":[136],"surpassing":[137],"G-CASCADE":[138],"MSA-2Net":[140],"by":[141],"1.82%":[142],"0.75%,":[144],"respectively.":[145],"Additionally,":[146],"delivers":[149],"competitive":[150],"results":[151,165],"ISIC2017":[154],"ISIC2018":[156],"datasets":[157],"across":[158],"DSC":[160],"mIoU":[162],"metrics.":[163],"These":[164],"underscore":[166],"promise":[168],"convolutional":[171],"designs":[172],"strong":[174],"contenders":[175],"CNN-":[177],"Transformer-driven":[179],"approaches":[180],"You":[185],"get":[187],"code":[189],"Github":[191],"https://anonymous.4open.science/r/SGraphNet":[192]},"counts_by_year":[],"updated_date":"2026-02-01T03:34:12.195049","created_date":"2026-01-30T00:00:00"}