{"id":"https://openalex.org/W2593523953","doi":"https://doi.org/10.1117/12.2252846","title":"Kernel descriptors for chest x-ray analysis","display_name":"Kernel descriptors for chest x-ray analysis","publication_year":2017,"publication_date":"2017-03-03","ids":{"openalex":"https://openalex.org/W2593523953","doi":"https://doi.org/10.1117/12.2252846","mag":"2593523953"},"language":"en","primary_location":{"id":"doi:10.1117/12.2252846","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2252846","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","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/A5044939556","display_name":"Gergely Gy. Orb\u00e1n","orcid":null},"institutions":[{"id":"https://openalex.org/I29770179","display_name":"Budapest University of Technology and Economics","ror":"https://ror.org/02w42ss30","country_code":"HU","type":"education","lineage":["https://openalex.org/I29770179"]}],"countries":["HU"],"is_corresponding":true,"raw_author_name":"Gergely Gy. Orb\u00e1n","raw_affiliation_strings":["Budapest Univ. of Technology and Economics (Hungary)"],"affiliations":[{"raw_affiliation_string":"Budapest Univ. of Technology and Economics (Hungary)","institution_ids":["https://openalex.org/I29770179"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048167453","display_name":"G\u00e1bor Horv\u00e1th","orcid":"https://orcid.org/0000-0001-9657-1219"},"institutions":[{"id":"https://openalex.org/I29770179","display_name":"Budapest University of Technology and Economics","ror":"https://ror.org/02w42ss30","country_code":"HU","type":"education","lineage":["https://openalex.org/I29770179"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"G\u00e1bor Horv\u00e1th","raw_affiliation_strings":["Budapest Univ. of Technology and Economics (Hungary)"],"affiliations":[{"raw_affiliation_string":"Budapest Univ. of Technology and Economics (Hungary)","institution_ids":["https://openalex.org/I29770179"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5044939556"],"corresponding_institution_ids":["https://openalex.org/I29770179"],"apc_list":null,"apc_paid":null,"fwci":0.2083,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.54642635,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"10134","issue":null,"first_page":"101343O","last_page":"101343O"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9965999722480774,"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"}},"topics":[{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9965999722480774,"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"}},{"id":"https://openalex.org/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.9954000115394592,"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/T10862","display_name":"AI in cancer detection","score":0.9883999824523926,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7454895973205566},{"id":"https://openalex.org/keywords/tomosynthesis","display_name":"Tomosynthesis","score":0.6715559363365173},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6652164459228516},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.654335618019104},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5673872828483582},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5214502215385437},{"id":"https://openalex.org/keywords/grayscale","display_name":"Grayscale","score":0.46406349539756775},{"id":"https://openalex.org/keywords/radiography","display_name":"Radiography","score":0.449273943901062},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.430441677570343},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4255879819393158},{"id":"https://openalex.org/keywords/gaussian-function","display_name":"Gaussian function","score":0.4160318374633789},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3911815285682678},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.34022438526153564},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.21492999792099},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.10854676365852356},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.09985369443893433},{"id":"https://openalex.org/keywords/mammography","display_name":"Mammography","score":0.09613808989524841}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7454895973205566},{"id":"https://openalex.org/C147454874","wikidata":"https://www.wikidata.org/wiki/Q7820316","display_name":"Tomosynthesis","level":5,"score":0.6715559363365173},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6652164459228516},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.654335618019104},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5673872828483582},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5214502215385437},{"id":"https://openalex.org/C78201319","wikidata":"https://www.wikidata.org/wiki/Q685727","display_name":"Grayscale","level":3,"score":0.46406349539756775},{"id":"https://openalex.org/C36454342","wikidata":"https://www.wikidata.org/wiki/Q245341","display_name":"Radiography","level":2,"score":0.449273943901062},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.430441677570343},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4255879819393158},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.4160318374633789},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3911815285682678},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34022438526153564},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.21492999792099},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.10854676365852356},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.09985369443893433},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.09613808989524841},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.0},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2252846","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2252846","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337351","display_name":"National Cancer Institute","ror":"https://ror.org/040gcmg81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1515999713","https://openalex.org/W1601795611","https://openalex.org/W1965885441","https://openalex.org/W1986649315","https://openalex.org/W2037886170","https://openalex.org/W2048005451","https://openalex.org/W2084108772","https://openalex.org/W2094977433","https://openalex.org/W2103444992","https://openalex.org/W2109743529","https://openalex.org/W2126326837","https://openalex.org/W2142422094","https://openalex.org/W2159727956","https://openalex.org/W2169501191","https://openalex.org/W4239517362","https://openalex.org/W6629510986","https://openalex.org/W6630863242","https://openalex.org/W6675541922","https://openalex.org/W6680941827","https://openalex.org/W6684876274","https://openalex.org/W6758827550","https://openalex.org/W6820743713","https://openalex.org/W7005390695"],"related_works":["https://openalex.org/W2375370983","https://openalex.org/W2369557298","https://openalex.org/W2545232906","https://openalex.org/W2011212036","https://openalex.org/W3044685708","https://openalex.org/W2116920932","https://openalex.org/W2065131986","https://openalex.org/W1545294102","https://openalex.org/W2127643120","https://openalex.org/W4229068138"],"abstract_inverted_index":{"In":[0],"this":[1],"study,":[2],"we":[3,72],"address":[4],"the":[5,27,42,59,64,79,120,123,126,131,187,206,212,216,220,238],"problem":[6],"of":[7,30,44,58,63,119],"lesion":[8,148],"classification":[9,28,149],"in":[10,55,122,151,202],"radiographic":[11],"scans.":[12,157,214],"We":[13,35,106,143],"adapt":[14],"image":[15,60,104,127],"kernel":[16,38,100,111,135],"functions":[17,39],"to":[18,25,82,92,138,219,230,235],"be":[19,53,93],"applicable":[20],"for":[21,205,211,237],"high-resolution,":[22],"grayscale":[23],"images":[24],"improve":[26],"accuracy":[29],"a":[31,74,98,108,139,162],"support":[32,140],"vector":[33,141],"machine.":[34],"take":[36],"existing":[37],"inspired":[40,77],"by":[41,78,180],"histogram":[43],"oriented":[45],"gradients,":[46],"and":[47,95,154,170,209,232],"derive":[48],"an":[49],"approximation":[50],"that":[51,114],"can":[52],"evaluated":[54],"linear":[56],"time":[57],"size":[61],"instead":[62],"original":[65],"quadratic":[66],"complexity,":[67],"enabling":[68],"highresolution":[69],"input.":[70,191],"Moreover,":[71],"propose":[73],"new":[75,88,193,217],"variant":[76],"matched":[80],"filter,":[81],"better":[83],"utilize":[84],"intensity":[85],"space.":[86],"The":[87,133,158,174,192],"kernels":[89,194,218],"are":[90],"improved":[91,224],"scale-invariant":[94],"combined":[96,134],"with":[97,167],"Gaussian":[99],"built":[101],"from":[102,161,186,228,233],"handcrafted":[103],"features.":[105],"introduce":[107],"simple":[109],"multiple":[110],"learning":[112],"framework":[113],"is":[115,136],"robust":[116],"when":[117],"one":[118],"kernels,":[121],"current":[124],"case":[125],"feature":[128],"kernel,":[129],"dominates":[130],"others.":[132],"input":[137],"classifier.":[142],"tested":[144],"our":[145],"method":[146],"on":[147],"both":[150],"chest":[152],"radiographs":[153,159],"digital":[155,175],"tomosynthesis":[156,176,213],"originated":[160],"database":[163,189,208],"including":[164],"364":[165],"patients":[166],"lung":[168],"nodules":[169],"150":[171],"healthy":[172],"cases.":[173],"scans":[177,185],"were":[178],"obtained":[179],"simulation":[181],"using":[182],"91":[183],"CT":[184],"LIDC-IDRI":[188],"as":[190],"showed":[195],"good":[196],"separation":[197],"capability:":[198],"ROC":[199],"AuC":[200,226],"was":[201],"[0.827,":[203],"0.853]":[204],"radiograph":[207],"0.763":[210],"Adding":[215],"image-feature-based":[221],"classifier":[222],"significantly":[223],"accuracy:":[225],"increased":[227],"0.958":[229],"0.967":[231],"0.788":[234],"0.801":[236],"two":[239],"applications.":[240]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}