{"id":"https://openalex.org/W7161830925","doi":"https://doi.org/10.1109/isbi61048.2026.11515868","title":"Entropy-Guided Agreement-Diversity: A Semi-Supervised Active Learning Framework for Fetal Head Segmentation in Ultrasound","display_name":"Entropy-Guided Agreement-Diversity: A Semi-Supervised Active Learning Framework for Fetal Head Segmentation in Ultrasound","publication_year":2026,"publication_date":"2026-04-08","ids":{"openalex":"https://openalex.org/W7161830925","doi":"https://doi.org/10.1109/isbi61048.2026.11515868"},"language":null,"primary_location":{"id":"doi:10.1109/isbi61048.2026.11515868","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi61048.2026.11515868","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI)","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/A5136558397","display_name":"Fangyijie Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210145784","display_name":"IBM Research - Ireland","ror":"https://ror.org/04jnxr720","country_code":"IE","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210145784"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Fangyijie Wang","raw_affiliation_strings":["Taighde &#x00C9;ireann - Research Ireland Centre for Research Training in Machine Learning,Ireland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Taighde &#x00C9;ireann - Research Ireland Centre for Research Training in Machine Learning,Ireland","institution_ids":["https://openalex.org/I4210145784"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136515878","display_name":"Siteng Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I4210145784","display_name":"IBM Research - Ireland","ror":"https://ror.org/04jnxr720","country_code":"IE","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210145784"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Siteng Ma","raw_affiliation_strings":["Taighde &#x00C9;ireann - Research Ireland Centre for Research Training in Machine Learning,Ireland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Taighde &#x00C9;ireann - Research Ireland Centre for Research Training in Machine Learning,Ireland","institution_ids":["https://openalex.org/I4210145784"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123681228","display_name":"Gu\u00e9nol\u00e9 Silvestre","orcid":null},"institutions":[{"id":"https://openalex.org/I100930933","display_name":"University College Dublin","ror":"https://ror.org/05m7pjf47","country_code":"IE","type":"education","lineage":["https://openalex.org/I100930933"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Gu\u00e9nol\u00e9 Silvestre","raw_affiliation_strings":["School of Computer Science, University College Dublin,Ireland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, University College Dublin,Ireland","institution_ids":["https://openalex.org/I100930933"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5136505622","display_name":"Kathleen M. Curran","orcid":null},"institutions":[{"id":"https://openalex.org/I4210145784","display_name":"IBM Research - Ireland","ror":"https://ror.org/04jnxr720","country_code":"IE","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210145784"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Kathleen M. Curran","raw_affiliation_strings":["Taighde &#x00C9;ireann - Research Ireland Centre for Research Training in Machine Learning,Ireland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Taighde &#x00C9;ireann - Research Ireland Centre for Research Training in Machine Learning,Ireland","institution_ids":["https://openalex.org/I4210145784"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.81011972,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12552","display_name":"Fetal and Pediatric Neurological Disorders","score":0.5228000283241272,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child 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/T12552","display_name":"Fetal and Pediatric Neurological Disorders","score":0.5228000283241272,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child 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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.11460000276565552,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.0674000009894371,"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":0.4860999882221222},{"id":"https://openalex.org/keywords/ultrasound","display_name":"Ultrasound","score":0.4650999903678894},{"id":"https://openalex.org/keywords/fetal-head","display_name":"Fetal head","score":0.4424000084400177},{"id":"https://openalex.org/keywords/active-contour-model","display_name":"Active contour model","score":0.4246000051498413},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.4068000018596649},{"id":"https://openalex.org/keywords/head","display_name":"Head (geology)","score":0.3111000061035156}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5648000240325928},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4860999882221222},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.47620001435279846},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.47600001096725464},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4661000072956085},{"id":"https://openalex.org/C143753070","wikidata":"https://www.wikidata.org/wiki/Q162564","display_name":"Ultrasound","level":2,"score":0.4650999903678894},{"id":"https://openalex.org/C2779811377","wikidata":"https://www.wikidata.org/wiki/Q5445900","display_name":"Fetal head","level":4,"score":0.4424000084400177},{"id":"https://openalex.org/C112353826","wikidata":"https://www.wikidata.org/wiki/Q127313","display_name":"Active contour model","level":4,"score":0.4246000051498413},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.4068000018596649},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.3294999897480011},{"id":"https://openalex.org/C2780312720","wikidata":"https://www.wikidata.org/wiki/Q5689100","display_name":"Head (geology)","level":2,"score":0.3111000061035156},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.29670000076293945},{"id":"https://openalex.org/C172680121","wikidata":"https://www.wikidata.org/wiki/Q26513","display_name":"Fetus","level":3,"score":0.29170000553131104},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.26510000228881836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi61048.2026.11515868","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi61048.2026.11515868","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI)","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":17,"referenced_works":["https://openalex.org/W2139037570","https://openalex.org/W2471138382","https://openalex.org/W2888303187","https://openalex.org/W2962804657","https://openalex.org/W2979907638","https://openalex.org/W3037518489","https://openalex.org/W3171581326","https://openalex.org/W3176195845","https://openalex.org/W3181236787","https://openalex.org/W4295934721","https://openalex.org/W4310027101","https://openalex.org/W4321232185","https://openalex.org/W4387826785","https://openalex.org/W4392655743","https://openalex.org/W4400617944","https://openalex.org/W4403071838","https://openalex.org/W4410295639"],"related_works":[],"abstract_inverted_index":{"Fetal":[0],"ultrasound":[1],"(US)":[2],"data":[3,142],"is":[4,25,163],"often":[5],"limited":[6,40],"due":[7],"to":[8,45,51,112],"privacy":[9],"and":[10,84,97,127,139,152],"regulatory":[11],"restrictions,":[12],"posing":[13],"challenges":[14],"for":[15,28,69,133,143],"training":[16],"deep":[17],"learning":[18,23,107],"(DL)":[19],"models.":[20],"While":[21],"semi-supervised":[22],"(SSL)":[24],"commonly":[26],"used":[27],"fetal":[29,70,134],"US":[30],"image":[31],"analysis,":[32],"existing":[33],"SSL":[34,102,150],"methods":[35],"typically":[36],"rely":[37],"on":[38,129,165],"random":[39],"selection,":[41],"which":[42],"can":[43],"lead":[44],"suboptimal":[46],"model":[47],"performance":[48],"by":[49],"overfitting":[50],"homogeneous":[52],"labeled":[53,141],"data.":[54,160],"To":[55],"address":[56],"this,":[57],"we":[58],"propose":[59],"a":[60,105],"two-stage":[61],"Active":[62],"Learning":[63],"(AL)":[64],"sampler,":[65],"Entropy-Guided":[66],"Agreement-Diversity":[67],"(EGAD),":[68],"head":[71,135],"segmentation.":[72],"Our":[73,146],"method":[74,147],"first":[75],"selects":[76],"the":[77,87,91],"most":[78],"uncertain":[79],"samples":[80],"using":[81,90,137],"predictive":[82],"entropy,":[83],"then":[85],"refines":[86],"final":[88],"selection":[89],"agreement-diversity":[92],"score":[93,124],"combining":[94],"cosine":[95],"similarity":[96],"mutual":[98],"information.":[99],"Additionally,":[100],"our":[101],"framework":[103],"employs":[104],"consistency":[106],"strategy":[108],"with":[109],"feature":[110],"downsampling":[111],"further":[113],"enhance":[114],"segmentation":[115],"performance.":[116],"In":[117],"experiments,":[118],"SSL-EGAD":[119],"achieves":[120],"an":[121],"average":[122],"Dice":[123],"of":[125],"94.57%":[126],"96.32%":[128],"two":[130],"public":[131],"datasets":[132],"segmentation,":[136],"5%":[138],"10%":[140],"training,":[144],"respectively.":[145],"outperforms":[148],"current":[149],"models":[151],"showcases":[153],"consistent":[154],"robustness":[155],"across":[156],"diverse":[157],"pregnancy":[158],"stage":[159],"The":[161],"code":[162],"available":[164],"GitHub.":[166]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-21T00:00:00"}
