{"id":"https://openalex.org/W2311041434","doi":"https://doi.org/10.1117/12.2217056","title":"Seamless lesion insertion in digital mammography: methodology and reader study","display_name":"Seamless lesion insertion in digital mammography: methodology and reader study","publication_year":2016,"publication_date":"2016-03-24","ids":{"openalex":"https://openalex.org/W2311041434","doi":"https://doi.org/10.1117/12.2217056","mag":"2311041434"},"language":"en","primary_location":{"id":"doi:10.1117/12.2217056","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2217056","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/A5074995495","display_name":"Aria Pezeshk","orcid":"https://orcid.org/0000-0002-3570-3051"},"institutions":[{"id":"https://openalex.org/I1320320070","display_name":"United States Food and Drug Administration","ror":"https://ror.org/034xvzb47","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1320320070"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Aria Pezeshk","raw_affiliation_strings":["U.S. Food and Drug Administration (United States)"],"affiliations":[{"raw_affiliation_string":"U.S. Food and Drug Administration (United States)","institution_ids":["https://openalex.org/I1320320070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006593300","display_name":"Nicholas Petrick","orcid":"https://orcid.org/0000-0001-5167-8899"},"institutions":[{"id":"https://openalex.org/I1320320070","display_name":"United States Food and Drug Administration","ror":"https://ror.org/034xvzb47","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1320320070"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nicholas Petrick","raw_affiliation_strings":["U.S. Food and Drug Administration  (United States)"],"affiliations":[{"raw_affiliation_string":"U.S. Food and Drug Administration  (United States)","institution_ids":["https://openalex.org/I1320320070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073468417","display_name":"Berkman Sahiner","orcid":"https://orcid.org/0000-0003-2804-2264"},"institutions":[{"id":"https://openalex.org/I1320320070","display_name":"United States Food and Drug Administration","ror":"https://ror.org/034xvzb47","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1320320070"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Berkman Sahiner","raw_affiliation_strings":["U.S. Food and Drug Administration (United States)"],"affiliations":[{"raw_affiliation_string":"U.S. Food and Drug Administration (United States)","institution_ids":["https://openalex.org/I1320320070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5074995495"],"corresponding_institution_ids":["https://openalex.org/I1320320070"],"apc_list":null,"apc_paid":null,"fwci":1.335,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.86560312,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"9785","issue":null,"first_page":"97850J","last_page":"97850J"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9998999834060669,"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":0.9998999834060669,"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/T11361","display_name":"Digital Radiography and Breast Imaging","score":0.9911999702453613,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9850000143051147,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.792668342590332},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7840540409088135},{"id":"https://openalex.org/keywords/mammography","display_name":"Mammography","score":0.7099897265434265},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5963172316551208},{"id":"https://openalex.org/keywords/digital-mammography","display_name":"Digital mammography","score":0.5928488969802856},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5449894070625305},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.4516732096672058},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40813302993774414},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3669658303260803},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35218286514282227},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.3038046360015869},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.23087626695632935},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.12340140342712402}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.792668342590332},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7840540409088135},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.7099897265434265},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5963172316551208},{"id":"https://openalex.org/C2781281974","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Digital mammography","level":5,"score":0.5928488969802856},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5449894070625305},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.4516732096672058},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40813302993774414},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3669658303260803},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35218286514282227},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.3038046360015869},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.23087626695632935},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.12340140342712402},{"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/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2217056","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2217056","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":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W177004468","https://openalex.org/W1976435141","https://openalex.org/W1988085117","https://openalex.org/W2002709260","https://openalex.org/W2088307774","https://openalex.org/W2108598243","https://openalex.org/W2110764733","https://openalex.org/W2149923130","https://openalex.org/W4251494088","https://openalex.org/W6607184829","https://openalex.org/W6647406277","https://openalex.org/W6651356205"],"related_works":["https://openalex.org/W2557931800","https://openalex.org/W2116047071","https://openalex.org/W54977395","https://openalex.org/W1993531705","https://openalex.org/W3197491245","https://openalex.org/W1966034269","https://openalex.org/W2000845075","https://openalex.org/W1766776469","https://openalex.org/W2029515485","https://openalex.org/W1990413141"],"abstract_inverted_index":{"Collection":[0],"of":[1,4,24,28,41,49,77,148,151,161,175,182,190],"large":[2,47],"repositories":[3],"clinical":[5,155,187,212],"images":[6],"containing":[7],"verified":[8],"cancer":[9],"locations":[10],"is":[11],"costly":[12],"and":[13,26,54],"time":[14],"consuming":[15],"due":[16],"to":[17,38,51,84,116,186],"difficulties":[18],"associated":[19],"with":[20],"both":[21],"the":[22,29,39,62,75,149,159,173,180,191,195],"accumulation":[23],"data":[25,50],"establishment":[27],"ground":[30],"truth.":[31],"This":[32,112],"problem":[33],"poses":[34],"a":[35,125,134,139,176],"significant":[36],"challenge":[37],"development":[40],"machine":[42],"learning":[43],"algorithms":[44,97],"that":[45,72,90,106,204],"require":[46],"amounts":[48],"properly":[52],"train":[53],"avoid":[55],"overfitting.":[56],"In":[57],"this":[58,152],"paper":[59],"we":[60,107,171],"expand":[61],"methods":[63],"in":[64,94,194],"our":[65,78],"previous":[66],"publications":[67],"by":[68,122],"making":[69],"several":[70],"modifications":[71],"significantly":[73],"increase":[74],"speed":[76],"insertion":[79],"algorithms,":[80],"thereby":[81],"allowing":[82],"them":[83],"be":[85,208],"used":[86],"for":[87,166],"inserting":[88,124],"lesions":[89,184,206],"are":[91],"much":[92],"larger":[93],"size.":[95],"These":[96],"have":[98,108],"been":[99],"incorporated":[100],"into":[101,138],"an":[102],"image":[103],"composition":[104],"tool":[105,113,153],"made":[109],"publicly":[110],"available.":[111],"allows":[114],"users":[115],"modify":[117],"or":[118,129],"supplement":[119],"existing":[120],"datasets":[121],"seamlessly":[123],"real":[126],"breast":[127],"mass":[128],"micro-calcification":[130],"cluster":[131],"extracted":[132],"from":[133,158,211],"source":[135],"digital":[136],"mammogram":[137],"different":[140],"location":[141],"on":[142,154],"another":[143],"mammogram.":[144],"We":[145],"demonstrate":[146],"examples":[147],"performance":[150],"cases":[156],"taken":[157],"University":[160],"South":[162],"Florida":[163],"Digital":[164],"Database":[165],"Screening":[167],"Mammography":[168],"(DDSM).":[169],"Finally,":[170],"report":[172],"results":[174],"reader":[177],"study":[178,196],"evaluating":[179],"realism":[181],"inserted":[183,205],"compared":[185],"lesions.":[188,213],"Analysis":[189],"radiologist":[192],"scores":[193],"using":[197],"receiver":[198],"operating":[199],"characteristic":[200],"(ROC)":[201],"methodology":[202],"indicates":[203],"cannot":[207],"reliably":[209],"distinguished":[210]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
