{"id":"https://openalex.org/W3011994505","doi":"https://doi.org/10.1117/12.2549520","title":"Unsupervised local feature learning for sensitive three-dimensional ultrasound assessment of carotid atherosclerosis","display_name":"Unsupervised local feature learning for sensitive three-dimensional ultrasound assessment of carotid atherosclerosis","publication_year":2020,"publication_date":"2020-03-10","ids":{"openalex":"https://openalex.org/W3011994505","doi":"https://doi.org/10.1117/12.2549520","mag":"3011994505"},"language":"en","primary_location":{"id":"doi:10.1117/12.2549520","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2549520","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2020: Image Processing","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/A5072615952","display_name":"Yuan Zhao","orcid":"https://orcid.org/0000-0002-2864-9210"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yuan Zhao","raw_affiliation_strings":["City Univ. of Hong Kong (Hong Kong, China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"City Univ. of Hong Kong (Hong Kong, China)","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016780725","display_name":"J. David Spence","orcid":"https://orcid.org/0000-0001-7478-1098"},"institutions":[{"id":"https://openalex.org/I4210105758","display_name":"Robarts Clinical Trials","ror":"https://ror.org/01e36dv41","country_code":"CA","type":"facility","lineage":["https://openalex.org/I125749732","https://openalex.org/I4210105758","https://openalex.org/I4405252475"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"J. David Spence","raw_affiliation_strings":["Robarts Research Institute (Canada)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robarts Research Institute (Canada)","institution_ids":["https://openalex.org/I4210105758"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051126889","display_name":"Bernard Chiu","orcid":"https://orcid.org/0000-0001-5237-2410"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Bernard Chiu","raw_affiliation_strings":["City Univ. of Hong Kong (Hong Kong, China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"City Univ. of Hong Kong (Hong Kong, China)","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"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":0.03780882,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"121","last_page":"121"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10816","display_name":"Cerebrovascular and Carotid Artery Diseases","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/T10816","display_name":"Cerebrovascular and Carotid Artery Diseases","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/T10924","display_name":"Cardiovascular Health and Disease Prevention","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"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/T12979","display_name":"Cardiovascular Disease and Adiposity","score":0.9797000288963318,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"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/medicine","display_name":"Medicine","score":0.5940322875976562},{"id":"https://openalex.org/keywords/biomarker","display_name":"Biomarker","score":0.5128129124641418},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5068925023078918},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4534001052379608},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41705191135406494},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.41128450632095337},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3879939615726471},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.33544865250587463},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3100338578224182},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.28207606077194214},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.2243644893169403},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.07824960350990295}],"concepts":[{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.5940322875976562},{"id":"https://openalex.org/C2781197716","wikidata":"https://www.wikidata.org/wiki/Q864574","display_name":"Biomarker","level":2,"score":0.5128129124641418},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5068925023078918},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4534001052379608},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41705191135406494},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.41128450632095337},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3879939615726471},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.33544865250587463},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3100338578224182},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.28207606077194214},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2243644893169403},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.07824960350990295},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2549520","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2549520","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2020: Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6399999856948853,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W4386815338","https://openalex.org/W2145836866","https://openalex.org/W2803255133"],"abstract_inverted_index":{"Sensitive":[0],"and":[1,15,28,59,154,214],"cost-effective":[2],"biomarkers":[3],"for":[4],"carotid":[5],"atherosclerosis":[6,19],"are":[7],"required":[8,229],"to":[9,39,61,64,98,124,232],"evaluate":[10],"the":[11,53,68,80,100,107,116,134,146,149,173,180,188,202,212],"efficacy":[12,203],"of":[13,52,83,106,130,172,179,187,201,204],"dietary":[14],"medical":[16],"treatments.":[17],"Carotid":[18],"is":[20,96],"a":[21,37,45,49,72,93,126,155,169,197,208],"focal":[22],"disease":[23],"predominantly":[24],"occurring":[25],"in":[26,85,133,192,196],"bends":[27],"bifurcations.":[29],"For":[30],"this":[31,89],"reason,":[32],"we":[33,91],"have":[34],"previously":[35],"developed":[36,58,92],"method":[38],"measure":[40],"local":[41,104],"vessel-wall-plus-plaque":[42],"thickness":[43],"(VWT);":[44],"biomarker":[46,94],"based":[47],"on":[48,71],"weighted":[50,170],"average":[51,171,182,190],"point-wise":[54,189],"\u0394VWT":[55,84],"was":[56,122,142,165,183,235],"also":[57],"validated":[60],"be":[62],"sensitive":[63],"treatment":[65],"effect.":[66],"However,":[67],"weight":[69],"determined":[70],"point-by-point":[73],"basis":[74],"did":[75],"not":[76,221],"take":[77],"into":[78],"account":[79],"spatial":[81],"correlation":[82,101],"neighboring":[86],"points.":[87],"In":[88],"paper,":[90],"that":[95,186,239],"able":[97],"characterize":[99],"within":[102],"each":[103,131],"patch":[105,132],"VWT":[108,136],"map.":[109,137],"The":[110,138,159,177,226],"deep":[111],"autoencoder":[112],"(DAE)":[113],"initialized":[114],"by":[115,144,163,167,230,240],"stacked":[117],"restricted":[118],"Boltzmann":[119],"machines":[120],"(RBMs)":[121],"introduced":[123],"learn":[125],"compact":[127],"feature":[128,140,175],"representation":[129],"2D":[135],"patch-based":[139,174,181],"change":[141],"obtained":[143,151],"taking":[145,168],"difference":[147,210],"between":[148,211],"features":[150],"at":[152],"baseline":[153],"follow-up":[156],"imaging":[157],"session.":[158],"new":[160],"biomarker,":[161],"denoted":[162],"\u2206VWT<sub>patch</sub>,":[164],"computed":[166],"change.":[176],"sensitivity":[178],"compared":[184],"with":[185],"(\u2206VWT<sub>point</sub>)":[191],"40":[193],"subjects":[194],"involved":[195],"placebo-controlled":[198],"clinical":[199],"trial":[200],"pomegranate.":[205],"\u2206VWT<sub>patch</sub>":[206,231],"detected":[207],"significant":[209],"pomegranate":[213],"placebo":[215],"groups":[216],"(p":[217,223],"=":[218,224],"0.017),":[219],"but":[220],"\u2206VWT<sub>point</sub>":[222],"0.056).":[225],"sample":[227],"size":[228],"establish":[233],"significance":[234],"37%":[236],"smaller":[237],"than":[238],"\u2206VWT<sub>point</sub>.":[241]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
