{"id":"https://openalex.org/W2084954972","doi":"https://doi.org/10.1117/12.2076633","title":"A new breast cancer risk analysis approach using features extracted from multiple sub-regions on bilateral mammograms","display_name":"A new breast cancer risk analysis approach using features extracted from multiple sub-regions on bilateral mammograms","publication_year":2015,"publication_date":"2015-03-20","ids":{"openalex":"https://openalex.org/W2084954972","doi":"https://doi.org/10.1117/12.2076633","mag":"2084954972"},"language":"en","primary_location":{"id":"doi:10.1117/12.2076633","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2076633","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/A5061443122","display_name":"Wenqing Sun","orcid":"https://orcid.org/0000-0002-7139-8867"},"institutions":[{"id":"https://openalex.org/I164936912","display_name":"The University of Texas at El Paso","ror":"https://ror.org/04d5vba33","country_code":"US","type":"education","lineage":["https://openalex.org/I164936912"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Wenqing Sun","raw_affiliation_strings":["The Univ. of Texas at El Paso (United States)","#N##TAB##TAB##TAB##TAB# The University of Texas at El Paso, United States#N##TAB##TAB##TAB#"],"affiliations":[{"raw_affiliation_string":"The Univ. of Texas at El Paso (United States)","institution_ids":["https://openalex.org/I164936912"]},{"raw_affiliation_string":"#N##TAB##TAB##TAB##TAB# The University of Texas at El Paso, United States#N##TAB##TAB##TAB#","institution_ids":["https://openalex.org/I164936912"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058784524","display_name":"Tzu-Liang Tseng","orcid":"https://orcid.org/0000-0002-3903-529X"},"institutions":[{"id":"https://openalex.org/I164936912","display_name":"The University of Texas at El Paso","ror":"https://ror.org/04d5vba33","country_code":"US","type":"education","lineage":["https://openalex.org/I164936912"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tzu-Liang B. Tseng","raw_affiliation_strings":["The Univ. of Texas at El Paso (United States)","#N##TAB##TAB##TAB##TAB# The University of Texas at El Paso, United States#N##TAB##TAB##TAB#"],"affiliations":[{"raw_affiliation_string":"The Univ. of Texas at El Paso (United States)","institution_ids":["https://openalex.org/I164936912"]},{"raw_affiliation_string":"#N##TAB##TAB##TAB##TAB# The University of Texas at El Paso, United States#N##TAB##TAB##TAB#","institution_ids":["https://openalex.org/I164936912"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045998635","display_name":"Bin Zheng","orcid":"https://orcid.org/0000-0002-7682-6648"},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bin Zheng","raw_affiliation_strings":["Northeastern Univ. (China)","The Univ. of Oklahoma (United States)"],"affiliations":[{"raw_affiliation_string":"Northeastern Univ. (China)","institution_ids":[]},{"raw_affiliation_string":"The Univ. of Oklahoma (United States)","institution_ids":["https://openalex.org/I8692664"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112266224","display_name":"Jianying Zhang","orcid":"https://orcid.org/0000-0002-1460-2817"},"institutions":[{"id":"https://openalex.org/I164936912","display_name":"The University of Texas at El Paso","ror":"https://ror.org/04d5vba33","country_code":"US","type":"education","lineage":["https://openalex.org/I164936912"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianying Zhang","raw_affiliation_strings":["Northeastern Univ. (China)","The Univ. of Texas at El Paso (United States)"],"affiliations":[{"raw_affiliation_string":"Northeastern Univ. (China)","institution_ids":[]},{"raw_affiliation_string":"The Univ. of Texas at El Paso (United States)","institution_ids":["https://openalex.org/I164936912"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100374757","display_name":"Wei Qian","orcid":"https://orcid.org/0000-0002-9563-721X"},"institutions":[{"id":"https://openalex.org/I164936912","display_name":"The University of Texas at El Paso","ror":"https://ror.org/04d5vba33","country_code":"US","type":"education","lineage":["https://openalex.org/I164936912"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Qian","raw_affiliation_strings":["Northeastern Univ. (China)","The Univ. of Texas at El Paso (United States)"],"affiliations":[{"raw_affiliation_string":"Northeastern Univ. (China)","institution_ids":[]},{"raw_affiliation_string":"The Univ. of Texas at El Paso (United States)","institution_ids":["https://openalex.org/I164936912"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5061443122"],"corresponding_institution_ids":["https://openalex.org/I164936912"],"apc_list":null,"apc_paid":null,"fwci":2.5886,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.91449187,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"9414","issue":null,"first_page":"941422","last_page":"941422"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9998000264167786,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9883999824523926,"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/T10885","display_name":"Gene expression and cancer classification","score":0.983299970626831,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.7744388580322266},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6738398671150208},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6342082023620605},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6317911148071289},{"id":"https://openalex.org/keywords/mammography","display_name":"Mammography","score":0.6252157688140869},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5854525566101074},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5847223401069641},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.48574429750442505},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.45415204763412476},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.44265204668045044},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.429085910320282},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.35913169384002686},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.3354809582233429},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.3029593229293823},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.06801968812942505}],"concepts":[{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.7744388580322266},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6738398671150208},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6342082023620605},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6317911148071289},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.6252157688140869},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5854525566101074},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5847223401069641},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.48574429750442505},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.45415204763412476},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.44265204668045044},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.429085910320282},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.35913169384002686},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.3354809582233429},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.3029593229293823},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.06801968812942505},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2076633","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2076633","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":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.8299999833106995}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1967385411","https://openalex.org/W1967468318","https://openalex.org/W1990368529","https://openalex.org/W2006447566","https://openalex.org/W2014915963","https://openalex.org/W2044465660","https://openalex.org/W2078276635","https://openalex.org/W2139921977","https://openalex.org/W2162207052","https://openalex.org/W2169961966","https://openalex.org/W2579520182","https://openalex.org/W4239510810","https://openalex.org/W6641868305"],"related_works":["https://openalex.org/W1514924336","https://openalex.org/W2002967116","https://openalex.org/W2024400191","https://openalex.org/W2024878248","https://openalex.org/W2379981957","https://openalex.org/W1976069075","https://openalex.org/W1975820156","https://openalex.org/W2351574028","https://openalex.org/W2437074470","https://openalex.org/W2390125918"],"abstract_inverted_index":{"A":[0,73],"novel":[1],"breast":[2,14,26,75,173,186],"cancer":[3,15,76,187],"risk":[4,16,77],"analysis":[5,17,78],"approach":[6],"is":[7,122],"proposed":[8],"for":[9],"enhancing":[10],"performance":[11,163],"of":[12,25,164],"computerized":[13,74],"using":[18,80,169,179],"bilateral":[19,37,48,88,181],"mammograms.":[20,113],"Based":[21],"on":[22,111],"the":[23,66,69,105,112,119,128,141,162,200],"intensity":[24],"area,":[27],"five":[28],"different":[29],"sub-regions":[30,159],"were":[31,39,145],"acquired":[32],"from":[33,41,50,157,171],"one":[34],"mammogram,":[35],"and":[36,62,93,99,107,140,147,192],"features":[38,131,155,170,182,194,197],"extracted":[40,156],"every":[42],"sub-region.":[43],"Our":[44],"dataset":[45],"includes":[46],"180":[47,51],"mammograms":[49],"women":[52],"who":[53],"underwent":[54],"routine":[55],"screening":[56,71],"examinations,":[57],"all":[58],"interpreted":[59],"as":[60],"negative":[61,142],"not":[63],"recalled":[64],"by":[65],"radiologists":[67],"during":[68],"original":[70],"procedures.":[72],"scheme":[79,166],"four":[81],"image":[82,101],"processing":[83],"modules,":[84],"including":[85],"sub-region":[86,130],"segmentation,":[87],"feature":[89,91,102],"extraction,":[90],"selection,":[92],"classification":[94,201],"was":[95],"designed":[96],"to":[97,132,168],"detect":[98],"compute":[100],"asymmetry":[103,180],"between":[104],"left":[106],"right":[108],"breasts":[109],"imaged":[110],"The":[114,136,150],"highest":[115],"computed":[116],"area":[117,174],"under":[118],"curve":[120],"(AUC)":[121],"0.763":[123],"\u00b1":[124],"0.021":[125],"when":[126],"applying":[127],"multiple":[129,158],"our":[133,165],"testing":[134],"dataset.":[135],"positive":[137],"predictive":[138,143],"value":[139,144],"0.60":[146],"0.73,":[148],"respectively.":[149],"study":[151],"demonstrates":[152],"that":[153],"(1)":[154],"can":[160,183,198],"improve":[161],"compared":[167],"whole":[172],"only;":[175],"(2)":[176],"a":[177],"classifier":[178],"effectively":[184],"predict":[185],"risk;":[188],"(3)":[189],"incorporating":[190],"texture":[191],"morphological":[193],"with":[195],"density":[196],"boost":[199],"accuracy.":[202]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}