{"id":"https://openalex.org/W7126052952","doi":"https://doi.org/10.1109/bibm66473.2025.11356795","title":"Towards Enhanced Lesion Segmentation Accuracy in Diabetic Foot Images","display_name":"Towards Enhanced Lesion Segmentation Accuracy in Diabetic Foot Images","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126052952","doi":"https://doi.org/10.1109/bibm66473.2025.11356795"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356795","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356795","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/A5028070882","display_name":"Yun ji Ban","orcid":null},"institutions":[{"id":"https://openalex.org/I142401562","display_name":"Electronics and Telecommunications Research Institute","ror":"https://ror.org/03ysstz10","country_code":"KR","type":"facility","lineage":["https://openalex.org/I142401562","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Yun Ji Ban","raw_affiliation_strings":["Research Institute,Electronics and Telecommunications,Gwangju,Korea"],"affiliations":[{"raw_affiliation_string":"Research Institute,Electronics and Telecommunications,Gwangju,Korea","institution_ids":["https://openalex.org/I142401562"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006407564","display_name":"Seihyoung Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I142401562","display_name":"Electronics and Telecommunications Research Institute","ror":"https://ror.org/03ysstz10","country_code":"KR","type":"facility","lineage":["https://openalex.org/I142401562","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seihyoung Lee","raw_affiliation_strings":["Research Institute,Electronics and Telecommunications,Gwangju,Korea"],"affiliations":[{"raw_affiliation_string":"Research Institute,Electronics and Telecommunications,Gwangju,Korea","institution_ids":["https://openalex.org/I142401562"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124273629","display_name":"Jeong Eun Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I142401562","display_name":"Electronics and Telecommunications Research Institute","ror":"https://ror.org/03ysstz10","country_code":"KR","type":"facility","lineage":["https://openalex.org/I142401562","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jeong Eun Kim","raw_affiliation_strings":["Research Institute,Electronics and Telecommunications,Gwangju,Korea"],"affiliations":[{"raw_affiliation_string":"Research Institute,Electronics and Telecommunications,Gwangju,Korea","institution_ids":["https://openalex.org/I142401562"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5028070882"],"corresponding_institution_ids":["https://openalex.org/I142401562"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.72581869,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7780","last_page":"7782"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11227","display_name":"Diabetic Foot Ulcer Assessment and Management","score":0.7483999729156494,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"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/T11227","display_name":"Diabetic Foot Ulcer Assessment and Management","score":0.7483999729156494,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"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/T11019","display_name":"Image Enhancement Techniques","score":0.039000000804662704,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.028999999165534973,"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/segmentation","display_name":"Segmentation","score":0.7336999773979187},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6430000066757202},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4957999885082245},{"id":"https://openalex.org/keywords/dice","display_name":"Dice","score":0.4426000118255615},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44110000133514404},{"id":"https://openalex.org/keywords/diabetic-foot","display_name":"Diabetic foot","score":0.415800005197525},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4115000069141388},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.3903000056743622}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7336999773979187},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.647599995136261},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6430000066757202},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5260999798774719},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4957999885082245},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.4426000118255615},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44110000133514404},{"id":"https://openalex.org/C2777858829","wikidata":"https://www.wikidata.org/wiki/Q52856","display_name":"Diabetic foot","level":3,"score":0.415800005197525},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4142000079154968},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4115000069141388},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.3903000056743622},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.38519999384880066},{"id":"https://openalex.org/C2778012447","wikidata":"https://www.wikidata.org/wiki/Q1034415","display_name":"Scope (computer science)","level":2,"score":0.38179999589920044},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.35420000553131104},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33500000834465027},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.33489999175071716},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.33230000734329224},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3215000033378601},{"id":"https://openalex.org/C2781156865","wikidata":"https://www.wikidata.org/wiki/Q827023","display_name":"Lesion","level":2,"score":0.2863999903202057},{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.2766000032424927},{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.27480000257492065},{"id":"https://openalex.org/C2780312720","wikidata":"https://www.wikidata.org/wiki/Q5689100","display_name":"Head (geology)","level":2,"score":0.25429999828338623},{"id":"https://openalex.org/C115076146","wikidata":"https://www.wikidata.org/wiki/Q1651051","display_name":"Foot (prosody)","level":2,"score":0.2531999945640564}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356795","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356795","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":[{"id":"https://openalex.org/G4013859747","display_name":null,"funder_award_id":"2022-0-00682","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"}],"funders":[{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2979708377","https://openalex.org/W3120430728","https://openalex.org/W3159481202","https://openalex.org/W4205264889","https://openalex.org/W4313156423","https://openalex.org/W4407257800","https://openalex.org/W4407258137"],"related_works":[],"abstract_inverted_index":{"Diabetic":[0],"foot":[1],"ulcer":[2],"(DFU)":[3],"instance":[4,159],"segmentation":[5],"remains":[6],"challenging":[7],"due":[8],"to":[9,31,52,66,111,120,131,147],"severe":[10],"class":[11],"imbalance,":[12],"boundary":[13],"sensitivity,":[14],"and":[15,70,97,117,134,150],"limited":[16],"labeled":[17],"data.":[18],"Building":[19],"on":[20,113,122],"our":[21],"first-place":[22],"DFUC":[23,46,102],"2024":[24,103],"winning":[25],"system,":[26],"we":[27,85],"propose":[28],"targeted":[29],"improvements":[30],"enhance":[32],"the":[33,40,82,87,101,105,114,123],"Dice":[34],"Similarity":[35],"Coefficient":[36],"(DSC).":[37],"We":[38],"used":[39],"original":[41],"2,000":[42],"image-mask":[43],"pairs":[44],"from":[45,109,118],"2024,":[47],"augmenting":[48],"800":[49],"training":[50,56],"images":[51],"obtain":[53],"4,000":[54],"effective":[55],"samples.":[57],"Our":[58],"improved":[59,107],"model":[60],"attaches":[61],"a":[62,67,72,92],"Mask":[63],"R-CNN":[64],"head":[65],"DINOv2":[68],"backbone":[69],"adopts":[71],"strategy":[73],"of":[74],"partial":[75],"fine-tuning.":[76],"To":[77],"better":[78],"align":[79],"optimization":[80],"with":[81,91],"evaluation":[83],"metric,":[84],"replace":[86],"default":[88],"mask":[89],"loss":[90,94,151],"composite":[93],"combining":[95],"BCE":[96],"soft":[98],"Dice.":[99],"On":[100],"leaderboards,":[104],"DSC":[106],"significantly:":[108],"0.6719":[110],"0.6986":[112],"Live":[115],"leaderboard":[116],"0.6519":[119],"0.6813":[121],"Validation":[124],"leaderboard.":[125],"Qualitative":[126],"analyses":[127],"confirm":[128],"tighter":[129],"alignment":[130],"ground":[132],"truth":[133],"reduced":[135],"symmetric-difference":[136],"(XOR)":[137],"error":[138],"regions.":[139],"These":[140],"results":[141],"demonstrate":[142],"that":[143],"principled,":[144],"low-overhead":[145],"adjustments":[146],"fine-tuning":[148],"scope":[149],"design":[152],"can":[153],"yield":[154],"measurable":[155],"gains":[156],"for":[157],"DFU":[158],"segmentation.":[160]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-01-30T00:00:00"}