{"id":"https://openalex.org/W7164156009","doi":"https://doi.org/10.48550/arxiv.2606.11106","title":"FADA: Accessible fetal ultrasound interpretation and annotation with a selectively distilled unified vision-language model","display_name":"FADA: Accessible fetal ultrasound interpretation and annotation with a selectively distilled unified vision-language model","publication_year":2026,"publication_date":"2026-06-09","ids":{"openalex":"https://openalex.org/W7164156009","doi":"https://doi.org/10.48550/arxiv.2606.11106"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.11106","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11106","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.11106","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136457290","display_name":"Mahmood Alzubaidi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alzubaidi, Mahmood","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138293072","display_name":"Uzair Shah","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shah, Uzair","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138370431","display_name":"Raden Muaz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Muaz, Raden","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049708885","display_name":"Ines Abbes","orcid":"https://orcid.org/0000-0001-6222-979X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abbes, Ines","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113897914","display_name":"Nader Mohammed","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohammed, Nader","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119245407","display_name":"Abdullatif Magram","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Magram, Abdullatif","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138346612","display_name":"Khalid Alyafei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alyafei, Khalid","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136665997","display_name":"Mowafa Househ","orcid":"https://orcid.org/0000-0002-3648-6271"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Househ, Mowafa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5013856888","display_name":"Marco Agus","orcid":"https://orcid.org/0000-0003-2752-3525"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Agus, Marco","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12552","display_name":"Fetal and Pediatric Neurological Disorders","score":0.7960000038146973,"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":0.7960000038146973,"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":0.0357000008225441,"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/T11184","display_name":"Neonatal and fetal brain pathology","score":0.03319999948143959,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.7530999779701233},{"id":"https://openalex.org/keywords/sonographer","display_name":"Sonographer","score":0.6470000147819519},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6061000227928162},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5573999881744385},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.5548999905586243},{"id":"https://openalex.org/keywords/pipeline-transport","display_name":"Pipeline transport","score":0.4124000072479248},{"id":"https://openalex.org/keywords/interpretation","display_name":"Interpretation (philosophy)","score":0.38269999623298645},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3391000032424927}],"concepts":[{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.7530999779701233},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7021999955177307},{"id":"https://openalex.org/C2778941581","wikidata":"https://www.wikidata.org/wiki/Q11251722","display_name":"Sonographer","level":3,"score":0.6470000147819519},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6342999935150146},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6061000227928162},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5573999881744385},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.5548999905586243},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.436599999666214},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.4124000072479248},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.38269999623298645},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3391000032424927},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33180001378059387},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C143753070","wikidata":"https://www.wikidata.org/wiki/Q162564","display_name":"Ultrasound","level":2,"score":0.2978000044822693},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28130000829696655},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.27889999747276306},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C194051981","wikidata":"https://www.wikidata.org/wiki/Q1337691","display_name":"Economic shortage","level":3,"score":0.26600000262260437},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2630000114440918},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.2554999887943268},{"id":"https://openalex.org/C101814296","wikidata":"https://www.wikidata.org/wiki/Q5439685","display_name":"Feature model","level":3,"score":0.2542000114917755},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2533000111579895},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.25290000438690186}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.11106","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11106","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.11106","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11106","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"A":[0],"global":[1],"shortage":[2],"of":[3,18,150],"trained":[4],"sonographers":[5],"limits":[6],"prenatal":[7],"ultrasound":[8,221],"screening":[9],"in":[10,34,142,203,228],"low-":[11],"and":[12,41,62,127,145,166,233],"middle-income":[13],"countries,":[14],"where":[15],"over":[16],"half":[17],"pregnant":[19],"women":[20],"receive":[21],"no":[22],"skilled":[23],"sonography.":[24],"Current":[25],"deep":[26],"learning":[27],"approaches":[28],"address":[29],"detection,":[30,61,126],"segmentation,":[31,122],"or":[32],"classification":[33],"isolation,":[35],"each":[36],"demanding":[37],"a":[38,49,65,162,182,211],"separate":[39],"model":[40,52,180],"expert-specified":[42],"labels":[43],"at":[44,237],"inference.":[45],"We":[46,171],"present":[47],"FADA,":[48],"unified":[50],"vision-language":[51],"built":[53],"on":[54,102,161,181],"Qwen3.5-VL":[55],"that":[56],"performs":[57],"clinical":[58],"interpretation,":[59],"classification,":[60],"segmentation":[63],"through":[64],"single":[66,163],"interpretation-first":[67],"pipeline":[68,202],"without":[69,168],"external":[70],"labels.":[71],"FADA":[72],"distills":[73],"knowledge":[74],"from":[75],"four":[76],"domain-specific":[77],"foundation":[78],"models":[79],"(FetalCLIP,":[80],"UltraSAM,":[81],"USF-MAE,":[82],"UltraFedFM)":[83],"via":[84],"offline":[85],"pre-computed":[86],"feature":[87,93],"caching.":[88],"Selective":[89],"distillation,":[90],"which":[91],"applies":[92],"alignment":[94],"only":[95],"to":[96],"annotation":[97],"tasks":[98],"while":[99],"interpretation":[100,130],"relies":[101],"standard":[103],"fine-tuning,":[104],"consistently":[105],"outperforms":[106],"full":[107,200],"distillation":[108],"across":[109,135],"most":[110],"evaluation":[111],"axes.":[112],"The":[113,157],"recommended":[114],"variant,":[115],"FADA-SKD,":[116],"achieves":[117],"0.8820":[118],"mean":[119],"Dice":[120],"for":[121,125,214],"0.7671":[123],"mAP@0.50":[124],"100%":[128],"structured":[129],"compliance.":[131],"Expert":[132],"sonographer":[133],"validation":[134],"237":[136],"images":[137],"confirms":[138],"clinically":[139],"acceptable":[140],"outputs":[141],"both":[143],"autonomous":[144],"human-in-the-loop":[146],"modes,":[147],"with":[148,195,219],"73.5%":[149],"interpretations":[151],"scoring":[152],"perfectly":[153],"under":[154],"clinician":[155],"guidance.":[156],"system":[158],"is":[159],"trainable":[160],"consumer":[164],"GPU":[165],"deployable":[167],"cloud":[169],"connectivity.":[170],"validate":[172],"edge":[173],"deployment":[174],"by":[175],"running":[176],"the":[177,199],"compressed":[178],"0.8B":[179],"commodity":[183],"smartphone":[184],"(Qualcomm":[185],"Snapdragon":[186],"7":[187],"Gen":[188],"1,":[189],"12":[190],"GB":[191],"RAM)":[192],"using":[193],"llama.cpp":[194],"GGUF":[196],"quantization,":[197],"completing":[198],"5-phase":[201],"approximately":[204],"60":[205],"seconds":[206],"entirely":[207],"offline.":[208],"This":[209],"establishes":[210],"practical":[212],"pathway":[213],"integrating":[215],"AI-assisted":[216],"fetal":[217],"assessment":[218],"portable":[220],"devices,":[222],"directly":[223],"addressing":[224],"diagnostic":[225],"access":[226],"gaps":[227],"resource-constrained":[229],"settings.":[230],"Code,":[231],"models,":[232],"data":[234],"are":[235],"available":[236],"https://github.com/mahmoodphd/FADA.":[238]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-11T00:00:00"}