{"id":"https://openalex.org/W7126098027","doi":"https://doi.org/10.1109/bibm66473.2025.11356122","title":"Active Learning for Meibomian Gland Segmentation in Infrared Meibography Images","display_name":"Active Learning for Meibomian Gland Segmentation in Infrared Meibography Images","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126098027","doi":"https://doi.org/10.1109/bibm66473.2025.11356122"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356122","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356122","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":"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/A5102307089","display_name":"Kunfeng Lai","orcid":null},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kunfeng Lai","raw_affiliation_strings":["College of Computer and Data Science, Fuzhou University,Fuzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer and Data Science, Fuzhou University,Fuzhou,China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124172164","display_name":"Dongqi Li","orcid":null},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dongqi Li","raw_affiliation_strings":["Feinberg School of Medicine, Northwestern University,Chicago,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Feinberg School of Medicine, Northwestern University,Chicago,USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ryan Benton","orcid":null},"institutions":[{"id":"https://openalex.org/I83809506","display_name":"University of South Alabama","ror":"https://ror.org/01s7b5y08","country_code":"US","type":"education","lineage":["https://openalex.org/I83809506"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ryan Benton","raw_affiliation_strings":["School of Computing, University of South Alabama,Mobile,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing, University of South Alabama,Mobile,USA","institution_ids":["https://openalex.org/I83809506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124163294","display_name":"Glen M. Borchert","orcid":null},"institutions":[{"id":"https://openalex.org/I83809506","display_name":"University of South Alabama","ror":"https://ror.org/01s7b5y08","country_code":"US","type":"education","lineage":["https://openalex.org/I83809506"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Glen Borchert","raw_affiliation_strings":["Frederick P. Whiddon College of Medicine, University of South Alabama,Mobile,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Frederick P. Whiddon College of Medicine, University of South Alabama,Mobile,USA","institution_ids":["https://openalex.org/I83809506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124240956","display_name":"Li Li","orcid":null},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Li","raw_affiliation_strings":["Fuzhou University Affiliated Provincial Hospital,Fuzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fuzhou University Affiliated Provincial Hospital,Fuzhou,China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083618757","display_name":"Jiawen Lin","orcid":"https://orcid.org/0000-0003-2950-1776"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiawen Lin","raw_affiliation_strings":["College of Computer and Data Science, Fuzhou University,Fuzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer and Data Science, Fuzhou University,Fuzhou,China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103191466","display_name":"Jingshan Huang","orcid":"https://orcid.org/0000-0003-2408-2883"},"institutions":[{"id":"https://openalex.org/I83809506","display_name":"University of South Alabama","ror":"https://ror.org/01s7b5y08","country_code":"US","type":"education","lineage":["https://openalex.org/I83809506"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jingshan Huang","raw_affiliation_strings":["School of Computing, University of South Alabama,Mobile,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing, University of South Alabama,Mobile,USA","institution_ids":["https://openalex.org/I83809506"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":"7251","last_page":"7258"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10997","display_name":"Ocular Surface and Contact Lens","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"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/T10997","display_name":"Ocular Surface and Contact Lens","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"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/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.0019000000320374966,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T10381","display_name":"Ophthalmology and Visual Impairment Studies","score":0.00039999998989515007,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"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":0.7300999760627747},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5199999809265137},{"id":"https://openalex.org/keywords/meibomian-gland","display_name":"Meibomian gland","score":0.5103999972343445},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.48980000615119934},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.46880000829696655},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4620000123977661},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43810001015663147},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4214000105857849}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7452999949455261},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7300999760627747},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5946999788284302},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5199999809265137},{"id":"https://openalex.org/C2777165675","wikidata":"https://www.wikidata.org/wiki/Q1425513","display_name":"Meibomian gland","level":3,"score":0.5103999972343445},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.48980000615119934},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.46880000829696655},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4620000123977661},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43810001015663147},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4214000105857849},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.3747999966144562},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37130001187324524},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33640000224113464},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.3271999955177307},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.29989999532699585},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.2606000006198883}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356122","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356122","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/W1995875735","https://openalex.org/W2956371155","https://openalex.org/W2964059111","https://openalex.org/W2986514296","https://openalex.org/W2998358834","https://openalex.org/W3007986608","https://openalex.org/W3147168483","https://openalex.org/W3164803610","https://openalex.org/W4283368640","https://openalex.org/W4296965557","https://openalex.org/W4386725051","https://openalex.org/W4391320571","https://openalex.org/W4392546474","https://openalex.org/W4401153684","https://openalex.org/W4401638586","https://openalex.org/W4404448120","https://openalex.org/W4404711690","https://openalex.org/W4407245874"],"related_works":[],"abstract_inverted_index":{"Meibomian":[0],"Gland":[1],"Dysfunction":[2],"(MGD)":[3],"is":[4,19,149,187,202],"a":[5,32,68,141,198],"key":[6],"contributor":[7],"to":[8,35,61,131,155,180,189,204,234],"clinical":[9],"Dry":[10],"Eye":[11],"Disease":[12],"(DED),":[13],"and":[14,66,92,160,178,243],"accurate":[15,242],"meibomian":[16,245],"gland":[17,246],"segmentation":[18,37,65,109,221],"essential":[20],"for":[21,48,82],"its":[22],"intelligent":[23],"diagnosis.":[24],"However,":[25,165],"the":[26,44,57,123,129,136,152,211,224],"high":[27],"cost":[28],"of":[29,59,119],"annotation":[30,226],"remains":[31],"major":[33],"barrier":[34],"improving":[36],"quality.":[38],"Active":[39],"Learning":[40],"(AL),":[41],"which":[42],"selects":[43],"most":[45],"informative":[46],"samples":[47],"annotation,":[49],"offers":[50],"an":[51,184],"efficient":[52,244],"solution.":[53],"This":[54],"study":[55],"explores":[56],"application":[58],"AL":[60,75,167],"infrared":[62],"meibography":[63,96],"image":[64],"introduces":[67],"novel":[69],"progressive":[70],"hybrid":[71],"sampling":[72,81,144,148,200],"framework.":[73],"Initial":[74],"stages":[76],"often":[77,232],"rely":[78],"on":[79,171,210],"random":[80,236],"initial":[83,124],"labeled":[84,125],"sets,":[85],"risking":[86],"unstable":[87],"performance":[88,222],"from":[89,135],"uninformative":[90],"data":[91,158,176],"cold":[93],"starts.":[94],"Additionally,":[95],"images":[97],"always":[98],"have":[99],"specular":[100,120],"reflections":[101,121],"whose":[102],"late":[103],"introduction":[104],"in":[105,151],"training":[106],"degrades":[107],"future":[108],"accuracy.":[110],"To":[111],"address":[112],"these":[113],"challenges,":[114],"we":[115],"incorporate":[116],"prior":[117],"knowledge":[118],"into":[122],"set":[126],"construction,":[127],"enabling":[128],"model":[130,163,206],"handle":[132],"such":[133],"artifacts":[134],"beginning.":[137],"We":[138],"then":[139],"implement":[140],"two-stage":[142],"dynamic":[143],"strategy:":[145],"entropy-based":[146],"uncertainty":[147,172],"used":[150],"early":[153],"iterations":[154],"maximize":[156],"annotated":[157],"informativeness":[159],"rapidly":[161],"boost":[162],"performance.":[164],"as":[166],"progresses,":[168],"only":[169],"focusing":[170],"may":[173],"overlook":[174],"global":[175],"distribution":[177],"lead":[179],"redundant":[181],"annotations.":[182],"Hence,":[183],"adaptive":[185],"threshold":[186],"introduced":[188],"monitor":[190],"sample":[191],"redundancy.":[192],"When":[193],"redundancy":[194],"exceeds":[195],"this":[196],"threshold,":[197],"diversity":[199],"module":[201],"activated":[203],"improve":[205],"generalization.":[207],"Experimental":[208],"results":[209],"public":[212],"MGD-1K":[213],"dataset":[214],"show":[215],"that":[216,231],"our":[217,238],"method":[218],"achieves":[219],"superior":[220],"under":[223],"same":[225],"budget.":[227],"Unlike":[228],"mainstream":[229],"methods":[230],"fail":[233],"outperform":[235],"baselines,":[237],"approach":[239],"consistently":[240],"delivers":[241],"segmentation.":[247]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-01-30T00:00:00"}