{"id":"https://openalex.org/W4415709077","doi":"https://doi.org/10.1109/icme59968.2025.11209244","title":"RLK-Net: An Efficient Residual Large Kernel Convolution with Channel-Wise Adaptive Feature Fusion for Medical Image Segmentation","display_name":"RLK-Net: An Efficient Residual Large Kernel Convolution with Channel-Wise Adaptive Feature Fusion for Medical Image Segmentation","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4415709077","doi":"https://doi.org/10.1109/icme59968.2025.11209244"},"language":null,"primary_location":{"id":"doi:10.1109/icme59968.2025.11209244","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11209244","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 Multimedia and Expo (ICME)","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/A5087004671","display_name":"Qingxue Zhao","orcid":"https://orcid.org/0000-0002-1915-0477"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingxue Zhao","raw_affiliation_strings":["Nankai University,College Of Software,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nankai University,College Of Software,China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102408537","display_name":"Zhongjie Pan","orcid":"https://orcid.org/0000-0002-0241-7884"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongjie Pan","raw_affiliation_strings":["Nankai University,College Of Software,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nankai University,College Of Software,China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087166824","display_name":"Di Wu","orcid":"https://orcid.org/0000-0002-6818-1886"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Di Wu","raw_affiliation_strings":["Nankai University,College Of Software,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nankai University,College Of Software,China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062825171","display_name":"Ge Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ge Tang","raw_affiliation_strings":["Nankai University,College Of Software,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nankai University,College Of Software,China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062590806","display_name":"Jun Tian","orcid":"https://orcid.org/0000-0003-2266-2892"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Tian","raw_affiliation_strings":["Nankai University,College Of Software,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nankai University,College Of Software,China","institution_ids":["https://openalex.org/I205237279"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I205237279"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.5489000082015991,"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":0.5489000082015991,"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/T10862","display_name":"AI in cancer detection","score":0.0982000008225441,"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":0.03880000114440918,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6310999989509583},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6211000084877014},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5812000036239624},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5784000158309937},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5253999829292297},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5253000259399414},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.517300009727478},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5105000138282776}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7511000037193298},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7429999709129333},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6310999989509583},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6211000084877014},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5812000036239624},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5784000158309937},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5253999829292297},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5253000259399414},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.517300009727478},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5105000138282776},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4959000051021576},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.46149998903274536},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.45590001344680786},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4041999876499176},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.40130001306533813},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.3894999921321869},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3513000011444092},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.34610000252723694},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.33090001344680786},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.30489999055862427},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme59968.2025.11209244","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11209244","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 Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2027685755","https://openalex.org/W2194775991","https://openalex.org/W2752782242","https://openalex.org/W2795374598","https://openalex.org/W2884436604","https://openalex.org/W2991372685","https://openalex.org/W3013198566","https://openalex.org/W3015788359","https://openalex.org/W3138516171","https://openalex.org/W3203480968","https://openalex.org/W4200015473","https://openalex.org/W4206706211","https://openalex.org/W4295934721","https://openalex.org/W4296425595","https://openalex.org/W4310595042","https://openalex.org/W4385346076","https://openalex.org/W4386362530","https://openalex.org/W4387211614","https://openalex.org/W4391390070","https://openalex.org/W4401749301"],"related_works":[],"abstract_inverted_index":{"While":[0],"U-Net":[1],"and":[2,52,64,81,110],"its":[3],"variants":[4],"perform":[5],"well":[6],"in":[7],"medical":[8,107,120],"image":[9,108,121],"segmentation,":[10],"their":[11],"encoders":[12],"have":[13],"limitations":[14],"that":[15,115],"hinder":[16],"the":[17,71,82],"capture":[18,66],"of":[19,84],"global":[20],"context.":[21],"Moreover,":[22],"traditional":[23],"skip":[24,85],"connections":[25],"do":[26],"not":[27],"fully":[28],"utilize":[29],"essential":[30],"features.":[31],"To":[32],"address":[33],"these":[34],"issues,":[35],"we":[36],"propose":[37],"a":[38],"novel":[39],"segmentation":[40,94,122],"architecture":[41],"called":[42],"RLK-Net,":[43],"which":[44],"integrates":[45],"Residual":[46],"Large":[47],"Kernel":[48],"Convolution":[49],"(RLK)":[50],"block":[51],"Channel-wise":[53],"Adaptive":[54],"Feature":[55],"Fusion":[56],"(CAFF)":[57],"module.":[58],"RLK-Net":[59,91,116],"effectively":[60],"enhances":[61],"feature":[62,79],"extraction":[63],"context":[65],"through":[67],"RLK":[68],"block,":[69],"while":[70,96],"CAFF":[72],"module":[73],"adaptively":[74],"adjusts":[75],"channel":[76],"importance,":[77],"optimizing":[78],"fusion":[80],"performance":[83,95],"connections.":[86],"Compared":[87],"to":[88],"Transformer-based":[89],"models,":[90],"achieves":[92],"high":[93],"significantly":[97],"reducing":[98],"computational":[99],"resource":[100],"requirements.":[101],"Experimental":[102],"evaluations":[103],"on":[104],"three":[105],"ultrasound":[106],"datasets":[109],"one":[111],"dermoscopy":[112],"dataset":[113],"demonstrate":[114],"outperforms":[117],"several":[118],"leading":[119],"models.":[123]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-30T00:00:00"}
