{"id":"https://openalex.org/W2921831249","doi":"https://doi.org/10.1117/12.2512604","title":"Reducing overfitting of a deep learning breast mass detection algorithm in mammography using synthetic images","display_name":"Reducing overfitting of a deep learning breast mass detection algorithm in mammography using synthetic images","publication_year":2019,"publication_date":"2019-03-13","ids":{"openalex":"https://openalex.org/W2921831249","doi":"https://doi.org/10.1117/12.2512604","mag":"2921831249"},"language":"en","primary_location":{"id":"doi:10.1117/12.2512604","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2512604","pdf_url":null,"source":{"id":"https://openalex.org/S4306519510","display_name":"Medical Imaging 2019: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2019: Computer-Aided Diagnosis","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":null,"display_name":"Kenny H. Cha","orcid":null},"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":"Kenny H. Cha","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":"middle","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":false,"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/A5029051784","display_name":"Christian Graff","orcid":"https://orcid.org/0000-0003-1564-3124"},"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":"Christian G. Graff","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/A5049685453","display_name":"Diksha Sharma","orcid":"https://orcid.org/0009-0003-2070-8153"},"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":"Diksha Sharma","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/A5085240330","display_name":"Andreu Badal","orcid":"https://orcid.org/0000-0002-1655-4177"},"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":"Andreu Badal","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/A5034816801","display_name":"Aldo Badano","orcid":"https://orcid.org/0000-0003-3712-6670"},"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":"Aldo Badano","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":8,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1320320070"],"apc_list":null,"apc_paid":null,"fwci":1.0116,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.82283573,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"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/T10862","display_name":"AI in cancer detection","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9979000091552734,"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/T10556","display_name":"Global Cancer Incidence and Screening","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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":0.9171923398971558},{"id":"https://openalex.org/keywords/mammography","display_name":"Mammography","score":0.8029680848121643},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7014434933662415},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6992058753967285},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.5893967151641846},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.551089882850647},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5461127161979675},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4851978123188019},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4671795964241028},{"id":"https://openalex.org/keywords/breast-imaging","display_name":"Breast imaging","score":0.4114408493041992},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3887830078601837},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.330968976020813},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.1941152811050415},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.10070541501045227}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.9171923398971558},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.8029680848121643},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7014434933662415},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6992058753967285},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.5893967151641846},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.551089882850647},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5461127161979675},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4851978123188019},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4671795964241028},{"id":"https://openalex.org/C2777432617","wikidata":"https://www.wikidata.org/wiki/Q22905905","display_name":"Breast imaging","level":5,"score":0.4114408493041992},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3887830078601837},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.330968976020813},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.1941152811050415},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.10070541501045227},{"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/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2512604","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2512604","pdf_url":null,"source":{"id":"https://openalex.org/S4306519510","display_name":"Medical Imaging 2019: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2019: Computer-Aided Diagnosis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1966084139","https://openalex.org/W2015056255","https://openalex.org/W2021732396","https://openalex.org/W2059192589","https://openalex.org/W2079684944","https://openalex.org/W2529908594","https://openalex.org/W2613718673","https://openalex.org/W2776937175","https://openalex.org/W2902699143","https://openalex.org/W4248875994","https://openalex.org/W4297792979","https://openalex.org/W6659364929","https://openalex.org/W6677587506"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W2922073769","https://openalex.org/W4297676672","https://openalex.org/W3122972499","https://openalex.org/W2105667964","https://openalex.org/W4322617773","https://openalex.org/W2076908053","https://openalex.org/W2117952144","https://openalex.org/W3012112151"],"abstract_inverted_index":{"We":[0,160,176],"evaluated":[1],"whether":[2],"using":[3,33,173,186],"synthetic":[4,89,207,220,231,245],"mammograms":[5,30,97,112,120,142,148,155,232],"for":[6,25,105,113,134,145,151,158,164,247,257],"training":[7,92,190,197,228,248],"data":[8,93,108,116,229],"augmentation":[9],"may":[10],"reduce":[11],"the":[12,18,55,62,84,123,126,178,182,188,196,219,227,237,241,244,250,253],"effects":[13],"of":[14,20,36,95,110,118,131,140,195,243,252],"overfitting":[15],"and":[16,43,61,70,103,138,153,192,205,217,239],"increase":[17],"performance":[19,180,251],"a":[21,34,107],"deep":[22,166,254],"learning":[23,167,255],"algorithm":[24,42,256],"breast":[26,40,48,58],"mass":[27,258],"detection.":[28],"Synthetic":[29],"were":[31,52,64,101,199],"generated":[32,102],"combination":[35],"an":[37],"in-silico":[38],"random":[39],"generation":[41],"x-ray":[44],"transport":[45,76],"simulation.":[46],"In-silico":[47],"phantoms":[49,86],"containing":[50],"masses":[51,63,100,124],"modeled":[53,65],"across":[54],"four":[56],"BI-RADS":[57],"density":[59],"categories,":[60],"with":[66,98,169,201,216,230],"different":[67],"sizes,":[68],"shapes":[69],"margins.":[71],"A":[72,91],"Monte":[73],"Carlo-based":[74],"xray":[75],"simulation":[77],"code,":[78],"MC-GPU,":[79],"was":[80,184,211],"used":[81,104,161],"to":[82,213],"project":[83],"3D":[85],"into":[87],"realistic":[88],"mammograms.":[90,208,221,261],"set":[94,109,117,198],"2,000":[96,206],"2,522":[99],"augmenting":[106],"real":[111,119],"training.":[114],"The":[115],"included":[121],"all":[122],"in":[125,235],"Curated":[127],"Breast":[128],"Imaging":[129],"Subset":[130],"Digital":[132],"Database":[133],"Screening":[135],"Mammography":[136],"(CBIS-DDSM)":[137],"consisted":[139],"1,112":[141],"(1,198":[143],"masses)":[144,150,157],"training,":[146],"120":[147],"(120":[149],"validation,":[152],"361":[154],"(378":[156],"testing.":[159],"Faster":[162],"R-CNN":[163],"our":[165],"network":[168,183],"pre-training":[170],"from":[171],"ImageNet":[172],"Resnet-101":[174],"architecture.":[175],"compared":[177],"detection":[179,259],"when":[181,193],"trained":[185],"only":[187],"CBIS-DDSM":[189],"images,":[191],"subsets":[194],"augmented":[200],"250,":[202],"500,":[203],"1,000":[204],"FROC":[209],"analysis":[210],"performed":[212],"compare":[214],"performances":[215],"without":[218],"Our":[222],"study":[223],"showed":[224],"that":[225,240],"enlarging":[226],"shows":[233],"promise":[234],"reducing":[236],"overfitting,":[238],"inclusion":[242],"images":[246],"increased":[249],"on":[260]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}
