{"id":"https://openalex.org/W2789603845","doi":"https://doi.org/10.1117/12.2294536","title":"Deep learning in breast cancer risk assessment: evaluation of fine-tuned convolutional neural networks on a clinical dataset of FFDMs","display_name":"Deep learning in breast cancer risk assessment: evaluation of fine-tuned convolutional neural networks on a clinical dataset of FFDMs","publication_year":2018,"publication_date":"2018-02-27","ids":{"openalex":"https://openalex.org/W2789603845","doi":"https://doi.org/10.1117/12.2294536","mag":"2789603845"},"language":"en","primary_location":{"id":"doi:10.1117/12.2294536","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2294536","pdf_url":null,"source":{"id":"https://openalex.org/S4306519508","display_name":"Medical Imaging 2018: 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 2018: Computer-Aided Diagnosis","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100423976","display_name":"Hui Li","orcid":"https://orcid.org/0000-0003-3139-2898"},"institutions":[{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hui Li","raw_affiliation_strings":["The Univ. of Chicago (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Univ. of Chicago (United States)","institution_ids":["https://openalex.org/I40347166"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049042648","display_name":"Maryellen L. Giger","orcid":"https://orcid.org/0000-0001-5482-9728"},"institutions":[{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maryellen L. Giger","raw_affiliation_strings":["The Univ. of Chicago (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Univ. of Chicago (United States)","institution_ids":["https://openalex.org/I40347166"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017524856","display_name":"Kayla R. Mendel","orcid":"https://orcid.org/0000-0002-4271-1711"},"institutions":[{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kayla R. Mendel","raw_affiliation_strings":["The Univ. of Chicago (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Univ. of Chicago (United States)","institution_ids":["https://openalex.org/I40347166"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064298275","display_name":"John K. Lee","orcid":"https://orcid.org/0000-0002-6570-2180"},"institutions":[{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John Lee","raw_affiliation_strings":["The Univ. of Chicago (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Univ. of Chicago (United States)","institution_ids":["https://openalex.org/I40347166"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100373015","display_name":"Lan Li","orcid":"https://orcid.org/0000-0002-3175-4169"},"institutions":[{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Li Lan","raw_affiliation_strings":["The Univ. of Chicago (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Univ. of Chicago (United States)","institution_ids":["https://openalex.org/I40347166"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I40347166"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"27","last_page":"27"},"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":0.9994999766349792,"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":0.9940000176429749,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7310512065887451},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.7239362001419067},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6748785972595215},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.639008641242981},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.6271735429763794},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5720803737640381},{"id":"https://openalex.org/keywords/risk-assessment","display_name":"Risk assessment","score":0.5145353674888611},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.4864158630371094},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42993229627609253},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37772223353385925},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.3349084258079529},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32728311419487},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.32657507061958313},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.21719077229499817}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7310512065887451},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.7239362001419067},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6748785972595215},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.639008641242981},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.6271735429763794},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5720803737640381},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.5145353674888611},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.4864158630371094},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42993229627609253},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37772223353385925},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.3349084258079529},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32728311419487},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.32657507061958313},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.21719077229499817},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2294536","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2294536","pdf_url":null,"source":{"id":"https://openalex.org/S4306519508","display_name":"Medical Imaging 2018: 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 2018: Computer-Aided Diagnosis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7599999904632568,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1970668319","https://openalex.org/W1992489994","https://openalex.org/W2003894496","https://openalex.org/W2007402398","https://openalex.org/W2035950812","https://openalex.org/W2061138960","https://openalex.org/W2089121733","https://openalex.org/W2089352475","https://openalex.org/W2099294562","https://openalex.org/W2133094542","https://openalex.org/W2137839901","https://openalex.org/W2156733130","https://openalex.org/W2163605009","https://openalex.org/W2189916683","https://openalex.org/W2570618306","https://openalex.org/W2919115771","https://openalex.org/W4299518610","https://openalex.org/W6637373629","https://openalex.org/W6682132143","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W4385649027","https://openalex.org/W4400094315","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"We":[0],"evaluated":[1,142],"the":[2,8,53,93,98,101,107,114,144,147,155,175,180,215],"potential":[3,226],"of":[4,10,85,87,127,157,166,177,217],"deep":[5],"learning":[6,197],"in":[7,100,154,174,230],"assessment":[9],"breast":[11,95,136],"cancer":[12,44,137,190,220,236],"risk":[13,138,221,237],"using":[14,143],"convolutional":[15],"neural":[16],"networks":[17],"(CNNs)":[18],"fine-tuned":[19,131],"on":[20,106,132],"full-field":[21],"digital":[22],"mammographic":[23,232],"(FFDM)":[24],"images.":[25],"This":[26],"study":[27],"included":[28],"456":[29],"clinical":[30],"FFDM":[31,59,133],"cases":[32],"from":[33,92,209],"two":[34],"high-risk":[35,118,160],"datasets:":[36],"BRCA1/2":[37,181],"gene-mutation":[38,182],"carriers":[39,183],"(53":[40],"cases)":[41],"and":[42,48,63,75,161,169,184,187,192,223],"unilateral":[43,189],"patients":[45,191],"(75":[46],"cases),":[47],"a":[49,70],"low-risk":[50,121,162,185,193],"dataset":[51,109],"as":[52,117,120],"control":[54],"group":[55],"(328":[56],"cases).":[57],"All":[58],"images":[60,115,134],"(12-bit":[61],"quantization":[62],"100":[64],"micron":[65],"pixel)":[66],"were":[67,76,90,172],"acquired":[68],"with":[69,198],"GE":[71],"Senographe":[72],"2000D":[73],"system":[74],"retrospectively":[77],"collected":[78],"under":[79,146],"an":[80],"IRB-approved,":[81],"HIPAA-compliant":[82],"protocol.":[83],"Regions":[84],"interest":[86],"256x256":[88],"pixels":[89],"selected":[91],"central":[94],"region":[96],"behind":[97],"nipple":[99],"craniocaudal":[102],"projection.":[103],"VGG19":[104,129],"pre-trained":[105,128],"ImageNet":[108],"was":[110,130,141],"used":[111],"to":[112,201,204,214,227],"classify":[113],"either":[116],"or":[119],"subjects.":[122,163],"The":[123],"last":[124],"fully-connected":[125],"layer":[126],"for":[135,235],"assessment.":[139,238],"Performance":[140],"area":[145],"receiver":[148],"operating":[149],"characteristic":[150],"(ROC)":[151],"curve":[152],"(AUC)":[153],"task":[156,176,216],"distinguishing":[158,178,218],"between":[159,179,188,219],"AUC":[164],"values":[165],"0.84":[167],"(SE=0.05)":[168],"0.72":[170],"(SE=0.06)":[171],"obtained":[173],"women":[186],"women,":[194],"respectively.":[195],"Deep":[196],"CNNs":[199],"appears":[200],"be":[202],"able":[203],"extract":[205],"parenchymal":[206,233],"characteristics":[207],"directly":[208],"FFDMs":[210],"which":[211],"are":[212],"relevant":[213],"populations,":[222],"therefore":[224],"has":[225],"aid":[228],"clinicians":[229],"assessing":[231],"patterns":[234]},"counts_by_year":[{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
