{"id":"https://openalex.org/W4401878778","doi":"https://doi.org/10.1109/access.2024.3449428","title":"Dual-Branch Fully Convolutional Segment Anything Model for Lesion Segmentation in Endoscopic Images","display_name":"Dual-Branch Fully Convolutional Segment Anything Model for Lesion Segmentation in Endoscopic Images","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4401878778","doi":"https://doi.org/10.1109/access.2024.3449428"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3449428","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3449428","pdf_url":null,"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":null,"license_id":null,"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://doi.org/10.1109/access.2024.3449428","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103848540","display_name":"Dongzhi He","orcid":"https://orcid.org/0000-0001-5142-9315"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Dongzhi He","raw_affiliation_strings":["Faculty of Information Technology, Beijing University of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104254022","display_name":"Zeyuan Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zeyuan Ma","raw_affiliation_strings":["Faculty of Information Technology, Beijing University of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100441349","display_name":"Chenxi Li","orcid":"https://orcid.org/0000-0002-7803-8282"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenxi Li","raw_affiliation_strings":["Faculty of Information Technology, Beijing University of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101668888","display_name":"Yunqi Li","orcid":"https://orcid.org/0000-0002-2841-1253"},"institutions":[{"id":"https://openalex.org/I2802939634","display_name":"Chinese PLA General Hospital","ror":"https://ror.org/04gw3ra78","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I2802939634"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunqi Li","raw_affiliation_strings":["Department of Gastroenterology, The First Medical Center, Chinese PLA General Hospital, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Gastroenterology, The First Medical Center, Chinese PLA General Hospital, Beijing, China","institution_ids":["https://openalex.org/I2802939634"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5103848540"],"corresponding_institution_ids":["https://openalex.org/I37796252"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.579,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.84477218,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"12","issue":null,"first_page":"125654","last_page":"125667"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9883000254631042,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9883000254631042,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9690999984741211,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9451000094413757,"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/computer-science","display_name":"Computer science","score":0.6704203486442566},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5794700980186462},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5703549385070801},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5471148490905762},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.48081672191619873},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38859403133392334},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3625210225582123}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6704203486442566},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5794700980186462},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5703549385070801},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5471148490905762},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.48081672191619873},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38859403133392334},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3625210225582123},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3449428","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3449428","pdf_url":null,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e6e371bb6e2e4b129a47d71900abd921","is_oa":true,"landing_page_url":"https://doaj.org/article/e6e371bb6e2e4b129a47d71900abd921","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 125654-125667 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3449428","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3449428","pdf_url":null,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.4099999964237213,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G2908271540","display_name":null,"funder_award_id":"62375287","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2008359794","https://openalex.org/W2145023731","https://openalex.org/W2194775991","https://openalex.org/W2464708700","https://openalex.org/W2752782242","https://openalex.org/W2798122215","https://openalex.org/W2884436604","https://openalex.org/W2899210047","https://openalex.org/W2914298543","https://openalex.org/W2997286550","https://openalex.org/W3015788359","https://openalex.org/W3127751679","https://openalex.org/W3128646645","https://openalex.org/W3134553072","https://openalex.org/W3168867926","https://openalex.org/W3176828726","https://openalex.org/W3204166336","https://openalex.org/W4224228275","https://openalex.org/W4226068227","https://openalex.org/W4226250457","https://openalex.org/W4294226146","https://openalex.org/W4299652825","https://openalex.org/W4312820606","https://openalex.org/W4319300502","https://openalex.org/W4364383051","https://openalex.org/W4366552071","https://openalex.org/W4385245566","https://openalex.org/W4388452200","https://openalex.org/W4389196682","https://openalex.org/W4390874575","https://openalex.org/W4391465264","https://openalex.org/W4391661489","https://openalex.org/W4393063770","https://openalex.org/W4394565119","https://openalex.org/W4402716243","https://openalex.org/W6748775820","https://openalex.org/W6750469568","https://openalex.org/W6763301180","https://openalex.org/W6796581206","https://openalex.org/W6838701581","https://openalex.org/W6851701011","https://openalex.org/W6851907948","https://openalex.org/W6862846459"],"related_works":["https://openalex.org/W2317351040","https://openalex.org/W4379231730","https://openalex.org/W2952466936","https://openalex.org/W4389858081","https://openalex.org/W1988622314","https://openalex.org/W2393949104","https://openalex.org/W4236344264","https://openalex.org/W3046201198","https://openalex.org/W4293061921","https://openalex.org/W1522196789"],"abstract_inverted_index":{"The":[0,186],"lesion":[1,13],"area":[2],"segmentation":[3,182,226],"technology":[4],"in":[5,53,66,110,213,223,232],"endoscopic":[6],"images":[7],"helps":[8],"doctors":[9],"discover":[10],"and":[11,75,108,123,142,146,161,178,195,203,228,235],"locate":[12],"areas,":[14],"which":[15,96,127],"is":[16],"crucial":[17],"for":[18,99],"improving":[19],"the":[20,29,35,47,62,100,104,115,129,144,150,171,181,191,198,210],"survival":[21],"rate":[22],"of":[23,31,103,149,175,184,197,225],"patients.":[24],"In":[25,152],"various":[26,220],"tasks":[27],"within":[28],"field":[30],"medical":[32,111,139],"image":[33,71,112,140],"segmentation,":[34],"fully":[36,48,105],"convolutional":[37,49,106],"network":[38,107],"with":[39],"U-shaped":[40],"architecture":[41],"has":[42,51,229],"achieved":[43],"great":[44,230],"success,":[45],"but":[46,81],"operation":[50],"limitations":[52],"obtaining":[54],"global":[55],"context":[56],"information.The":[57],"Segment":[58],"Anything":[59],"Model":[60],"(SAM),":[61],"latest":[63],"foundational":[64],"model":[65,199,211],"computer":[67],"vision,":[68],"boasts":[69],"powerful":[70],"feature":[72],"extraction":[73],"capabilities":[74],"can":[76],"capture":[77,84],"explicit":[78],"long-range":[79],"dependencies,":[80],"it":[82],"cannot":[83,134],"low-level":[85],"detailed":[86],"information.":[87],"This":[88],"paper":[89,119,215],"proposes":[90,120],"a":[91,121,157,162],"dual-branch":[92],"model,":[93,177],"named":[94],"FCSAM,":[95],"makes":[97],"up":[98],"respective":[101],"shortcomings":[102],"SAM":[109],"segmentation.":[113],"At":[114],"same":[116],"time,":[117],"this":[118,176,214],"LayerNorm":[122],"low-rank-based(LNLoRA)":[124],"fine-tuning":[125],"strategy,":[126],"overcomes":[128],"problem":[130],"that":[131,190,209],"large":[132],"models":[133],"be":[135],"applied":[136],"to":[137,169,219],"small-scale":[138],"datasets":[141],"reduces":[143],"deployment":[145],"storage":[147],"overhead":[148],"model.":[151],"addition,":[153],"we":[154],"also":[155],"design":[156],"boundary":[158,163,172],"aware":[159],"module":[160],"key":[164],"point":[165],"map":[166],"generation":[167],"algorithm":[168],"improve":[170,180],"localization":[173],"ability":[174],"significantly":[179],"performance":[183,218],"lesions.":[185],"experimental":[187],"results":[188],"show":[189,208],"IOU,":[192],"Dice":[193],"coefficient":[194],"accuracy":[196,227],"are":[200],"79.3%,":[201],"88.1%":[202],"93.2%":[204],"respectively.":[205],"These":[206],"metrics":[207],"proposed":[212],"achieves":[216],"superior":[217],"competing":[221],"methods":[222],"terms":[224],"potential":[231],"clinical":[233],"analysis":[234],"diagnosis.":[236]},"counts_by_year":[{"year":2025,"cited_by_count":6}],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}