{"id":"https://openalex.org/W4387092551","doi":"https://doi.org/10.1109/access.2023.3320064","title":"Self Reinforcing Multi-Class Transformer for Kidney Glomerular Basement Membrane Segmentation","display_name":"Self Reinforcing Multi-Class Transformer for Kidney Glomerular Basement Membrane Segmentation","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4387092551","doi":"https://doi.org/10.1109/access.2023.3320064"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3320064","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3320064","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10265254.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10265254.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102737137","display_name":"Shaoshuai Yan","orcid":"https://orcid.org/0009-0000-5573-7489"},"institutions":[{"id":"https://openalex.org/I94310126","display_name":"Shanxi Normal University","ror":"https://ror.org/03zd3ta61","country_code":"CN","type":"education","lineage":["https://openalex.org/I94310126"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaoshuai Yan","raw_affiliation_strings":["Shanxi Normal University, Taiyuan, China"],"raw_orcid":"https://orcid.org/0009-0000-5573-7489","affiliations":[{"raw_affiliation_string":"Shanxi Normal University, Taiyuan, China","institution_ids":["https://openalex.org/I94310126"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034994052","display_name":"Xiangsheng Huang","orcid":"https://orcid.org/0009-0007-5972-5793"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangsheng Huang","raw_affiliation_strings":["Xiongan Institute of Innovation, Chinese Academy of Sciences, Xiong&#x2019;an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiongan Institute of Innovation, Chinese Academy of Sciences, Xiong&#x2019;an, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100686312","display_name":"Wei Lian","orcid":"https://orcid.org/0000-0001-6917-7451"},"institutions":[{"id":"https://openalex.org/I115067436","display_name":"Changzhi University","ror":"https://ror.org/04svmxd14","country_code":"CN","type":"education","lineage":["https://openalex.org/I115067436"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Lian","raw_affiliation_strings":["Changzhi University, Changzhi, China"],"raw_orcid":"https://orcid.org/0000-0001-6917-7451","affiliations":[{"raw_affiliation_string":"Changzhi University, Changzhi, China","institution_ids":["https://openalex.org/I115067436"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013285800","display_name":"Caifang Song","orcid":"https://orcid.org/0000-0001-8214-9586"},"institutions":[{"id":"https://openalex.org/I94310126","display_name":"Shanxi Normal University","ror":"https://ror.org/03zd3ta61","country_code":"CN","type":"education","lineage":["https://openalex.org/I94310126"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Caifang Song","raw_affiliation_strings":["Shanxi Normal University, Taiyuan, China"],"raw_orcid":"https://orcid.org/0000-0001-8214-9586","affiliations":[{"raw_affiliation_string":"Shanxi Normal University, Taiyuan, China","institution_ids":["https://openalex.org/I94310126"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.2474,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.80412771,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"11","issue":null,"first_page":"105892","last_page":"105901"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.9987999796867371,"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"}},"topics":[{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.9987999796867371,"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":0.9975000023841858,"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":0.9973999857902527,"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":0.7257837653160095},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6996232867240906},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6268041133880615},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5986080169677734},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5378619432449341},{"id":"https://openalex.org/keywords/glomerular-basement-membrane","display_name":"Glomerular basement membrane","score":0.4605301022529602},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44360631704330444},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3388727903366089},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.20329326391220093}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7257837653160095},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6996232867240906},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6268041133880615},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5986080169677734},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5378619432449341},{"id":"https://openalex.org/C2776115139","wikidata":"https://www.wikidata.org/wiki/Q1753456","display_name":"Glomerular basement membrane","level":4,"score":0.4605301022529602},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44360631704330444},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3388727903366089},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.20329326391220093},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C134018914","wikidata":"https://www.wikidata.org/wiki/Q162606","display_name":"Endocrinology","level":1,"score":0.0},{"id":"https://openalex.org/C2780091579","wikidata":"https://www.wikidata.org/wiki/Q9377","display_name":"Kidney","level":2,"score":0.0},{"id":"https://openalex.org/C2780368995","wikidata":"https://www.wikidata.org/wiki/Q605006","display_name":"Glomerulonephritis","level":3,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3320064","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3320064","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10265254.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:ffd0d9fd2c0345b898ca24e657aeb7ad","is_oa":true,"landing_page_url":"https://doaj.org/article/ffd0d9fd2c0345b898ca24e657aeb7ad","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 105892-105901 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3320064","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3320064","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10265254.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G92300158","display_name":null,"funder_award_id":"2020YFC2006400","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387092551.pdf","grobid_xml":"https://content.openalex.org/works/W4387092551.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W55302487","https://openalex.org/W1901129140","https://openalex.org/W1968219338","https://openalex.org/W1971294694","https://openalex.org/W2065811028","https://openalex.org/W2066523658","https://openalex.org/W2084359806","https://openalex.org/W2108598243","https://openalex.org/W2162737586","https://openalex.org/W2302388243","https://openalex.org/W2963868681","https://openalex.org/W2964080601","https://openalex.org/W2983446232","https://openalex.org/W2997286550","https://openalex.org/W2999580839","https://openalex.org/W3092344722","https://openalex.org/W3112687452","https://openalex.org/W3138516171","https://openalex.org/W3169865585","https://openalex.org/W3175515048","https://openalex.org/W3196479321","https://openalex.org/W3196904463","https://openalex.org/W3204166336","https://openalex.org/W4226068227","https://openalex.org/W4287225564","https://openalex.org/W4297775537","https://openalex.org/W6602288470","https://openalex.org/W6639824700","https://openalex.org/W6737664043"],"related_works":["https://openalex.org/W2402761219","https://openalex.org/W2785900585","https://openalex.org/W2353730437","https://openalex.org/W2490303674","https://openalex.org/W2609066826","https://openalex.org/W2810752900","https://openalex.org/W3186538219","https://openalex.org/W2365677836","https://openalex.org/W2531295127","https://openalex.org/W2890372105"],"abstract_inverted_index":{"The":[0],"precise":[1],"segmentation":[2,43,54,142],"of":[3,44,135,143,156,159],"the":[4,26,29,58,125,133,136,141,153,160,163,167,181,186,193,204],"glomerular":[5],"basement":[6],"membrane":[7],"(GBM)":[8],"can":[9,76,139],"aid":[10],"pathologists":[11],"in":[12,162],"making":[13],"accurate":[14],"pathological":[15],"diagnoses.":[16],"However,":[17],"conventional":[18],"methods":[19],"solely":[20],"focus":[21,170,183],"on":[22],"segmenting":[23],"GBM":[24,32,63,80,144],"from":[25],"background,":[27],"disregarding":[28],"interconnections":[30,61],"between":[31,62],"and":[33,60,64,120,199],"its":[34,65],"similar":[35,66],"surrounding":[36,67],"tissues,":[37],"which":[38,122],"leads":[39],"to":[40,56,90,169],"imprecise":[41],"boundary":[42],"GBM.":[45],"To":[46,108],"address":[47,109],"this":[48,74,110],"issue,":[49,111],"we":[50,112,123,151],"employed":[51],"a":[52,114],"multi-category":[53],"method":[55,75,195],"model":[57,161,168],"distinctions":[59],"tissues.":[68],"Our":[69,188],"experimental":[70,189],"results":[71,130,190],"demonstrate":[72],"that":[73,97,132,192],"more":[77,115,147],"accurately":[78],"segment":[79],"with":[81],"blurred":[82],"boundaries.":[83,149],"Historically,":[84],"scholars":[85],"have":[86],"primarily":[87],"used":[88],"convolution":[89,119],"build":[91],"models.":[92],"This":[93],"approach":[94],"has":[95,196],"limitation":[96],"only":[98],"local":[99],"information":[100,172],"is":[101],"modeled":[102],"without":[103],"effectively":[104],"extracting":[105],"global":[106],"information.":[107],"propose":[113],"reasonable":[116],"structure":[117],"combining":[118],"attention,":[121],"call":[124],"Self-Reinforcing":[126],"Attention":[127],"Mechanism.":[128],"Experimental":[129],"indicate":[131],"addition":[134],"attention":[137],"mechanism":[138],"help":[140],"by":[145,184],"yielding":[146],"continuous":[148],"Finally,":[150],"incorporate":[152],"feature":[154],"maps":[155],"each":[157],"layer":[158],"loss":[164],"function,":[165],"allowing":[166],"semantic":[171],"at":[173],"varying":[174],"scales":[175],"while":[176],"also":[177],"providing":[178],"control":[179],"over":[180],"model\u2019s":[182],"adjusting":[185],"weight.":[187],"demonstrated":[191],"proposed":[194],"higher":[197],"performance":[198],"better":[200],"generalization":[201],"ability":[202],"than":[203],"state-of-the-art":[205],"approaches.":[206]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
