{"id":"https://openalex.org/W2877300758","doi":"https://doi.org/10.1117/12.2318464","title":"Using a convolutional neural network to predict readers' estimates of mammographic density for breast cancer risk assessment","display_name":"Using a convolutional neural network to predict readers' estimates of mammographic density for breast cancer risk assessment","publication_year":2018,"publication_date":"2018-07-06","ids":{"openalex":"https://openalex.org/W2877300758","doi":"https://doi.org/10.1117/12.2318464","mag":"2877300758"},"language":"en","primary_location":{"id":"doi:10.1117/12.2318464","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2318464","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"14th International Workshop on Breast Imaging (IWBI 2018)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://research.manchester.ac.uk/en/publications/5fbfcbfc-4711-489c-885c-979cd4082a35","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084939027","display_name":"Martin Fergie","orcid":"https://orcid.org/0000-0002-9531-6109"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Martin Fergie","raw_affiliation_strings":["The Univ. of Manchester (United Kingdom)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Univ. of Manchester (United Kingdom)","institution_ids":["https://openalex.org/I28407311"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027130627","display_name":"Michael Berks","orcid":"https://orcid.org/0000-0003-4727-2006"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Michael Berks","raw_affiliation_strings":["The Univ. of Manchester (United Kingdom)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Univ. of Manchester (United Kingdom)","institution_ids":["https://openalex.org/I28407311"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050686707","display_name":"Elaine F. Harkness","orcid":"https://orcid.org/0000-0001-6625-7739"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]},{"id":"https://openalex.org/I4210092852","display_name":"Manchester University NHS Foundation Trust","ror":"https://ror.org/00he80998","country_code":"GB","type":"healthcare","lineage":["https://openalex.org/I4210092852"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Elaine  F. Harkness","raw_affiliation_strings":["Manchester Univ. NHS Foundation Trust (United Kingdom)","The Univ. of Manchester (United Kingdom)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Manchester Univ. NHS Foundation Trust (United Kingdom)","institution_ids":["https://openalex.org/I4210092852"]},{"raw_affiliation_string":"The Univ. of Manchester (United Kingdom)","institution_ids":["https://openalex.org/I28407311"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113042086","display_name":"Susan Astley","orcid":"https://orcid.org/0000-0002-2989-2765"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]},{"id":"https://openalex.org/I4210092852","display_name":"Manchester University NHS Foundation Trust","ror":"https://ror.org/00he80998","country_code":"GB","type":"healthcare","lineage":["https://openalex.org/I4210092852"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Susan Astley","raw_affiliation_strings":["Manchester Univ. NHS Foundation Trust (United Kingdom)","The Univ. of Manchester (United Kingdom)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Manchester Univ. NHS Foundation Trust (United Kingdom)","institution_ids":["https://openalex.org/I4210092852"]},{"raw_affiliation_string":"The Univ. of Manchester (United Kingdom)","institution_ids":["https://openalex.org/I28407311"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078274616","display_name":"Johan Hulleman","orcid":"https://orcid.org/0000-0001-6900-5190"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Johan Hulleman","raw_affiliation_strings":["School of Biological Sciences, Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Biological Sciences, Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK","institution_ids":["https://openalex.org/I28407311"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080899656","display_name":"Adam R. Brentnall","orcid":"https://orcid.org/0000-0001-6327-4357"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Adam R. Brentnall","raw_affiliation_strings":["Queen Mary Univ. of London (United Kingdom)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Queen Mary Univ. of London (United Kingdom)","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022237789","display_name":"Jack Cuzick","orcid":"https://orcid.org/0000-0001-7420-7512"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jack Cuzick","raw_affiliation_strings":["Queen Mary Univ. of London (United Kingdom)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Queen Mary Univ. of London (United Kingdom)","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087003938","display_name":"D. Gareth Evans","orcid":"https://orcid.org/0000-0002-8482-5784"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]},{"id":"https://openalex.org/I4210092852","display_name":"Manchester University NHS Foundation Trust","ror":"https://ror.org/00he80998","country_code":"GB","type":"healthcare","lineage":["https://openalex.org/I4210092852"]},{"id":"https://openalex.org/I4210133995","display_name":"The Christie NHS Foundation Trust","ror":"https://ror.org/03v9efr22","country_code":"GB","type":"funder","lineage":["https://openalex.org/I4210133995"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Gareth Evans","raw_affiliation_strings":["Manchester Univ. NHS Foundation Trust (United Kingdom)","The Christie NHS Foundation Trust (United Kingdom)","The Univ. of Manchester (United Kingdom)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Manchester Univ. NHS Foundation Trust (United Kingdom)","institution_ids":["https://openalex.org/I4210092852"]},{"raw_affiliation_string":"The Christie NHS Foundation Trust (United Kingdom)","institution_ids":["https://openalex.org/I4210133995"]},{"raw_affiliation_string":"The Univ. of Manchester (United Kingdom)","institution_ids":["https://openalex.org/I28407311"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070643124","display_name":"Georgia Ionescu","orcid":null},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Georgia V. Ionescu","raw_affiliation_strings":["The Univ. of Manchester (United Kingdom)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Univ. of Manchester (United Kingdom)","institution_ids":["https://openalex.org/I28407311"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":9,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.169,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.59584144,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"4","issue":null,"first_page":"70","last_page":"70"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9994000196456909,"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":0.9842000007629395,"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/concordance","display_name":"Concordance","score":0.6739169359207153},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6690055131912231},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.6233193278312683},{"id":"https://openalex.org/keywords/bi-rads","display_name":"BI-RADS","score":0.6222598552703857},{"id":"https://openalex.org/keywords/concordance-correlation-coefficient","display_name":"Concordance correlation coefficient","score":0.5800927877426147},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.5620077252388},{"id":"https://openalex.org/keywords/mammography","display_name":"Mammography","score":0.5533719062805176},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.5002791881561279},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4820312261581421},{"id":"https://openalex.org/keywords/mammographic-density","display_name":"MAMMOGRAPHIC DENSITY","score":0.45667359232902527},{"id":"https://openalex.org/keywords/visual-analogue-scale","display_name":"Visual analogue scale","score":0.43806952238082886},{"id":"https://openalex.org/keywords/digital-mammography","display_name":"Digital mammography","score":0.4349234402179718},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3802533745765686},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.32507598400115967},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2815512418746948},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.28147023916244507},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22962293028831482},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.19114580750465393},{"id":"https://openalex.org/keywords/surgery","display_name":"Surgery","score":0.11167186498641968}],"concepts":[{"id":"https://openalex.org/C160798450","wikidata":"https://www.wikidata.org/wiki/Q4230870","display_name":"Concordance","level":2,"score":0.6739169359207153},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6690055131912231},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.6233193278312683},{"id":"https://openalex.org/C2779098232","wikidata":"https://www.wikidata.org/wiki/Q903975","display_name":"BI-RADS","level":5,"score":0.6222598552703857},{"id":"https://openalex.org/C2781059462","wikidata":"https://www.wikidata.org/wiki/Q5158906","display_name":"Concordance correlation coefficient","level":2,"score":0.5800927877426147},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.5620077252388},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.5533719062805176},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.5002791881561279},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4820312261581421},{"id":"https://openalex.org/C2909213482","wikidata":"https://www.wikidata.org/wiki/Q17011492","display_name":"MAMMOGRAPHIC DENSITY","level":5,"score":0.45667359232902527},{"id":"https://openalex.org/C14184104","wikidata":"https://www.wikidata.org/wiki/Q1362526","display_name":"Visual analogue scale","level":2,"score":0.43806952238082886},{"id":"https://openalex.org/C2781281974","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Digital mammography","level":5,"score":0.4349234402179718},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3802533745765686},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.32507598400115967},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2815512418746948},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.28147023916244507},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22962293028831482},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.19114580750465393},{"id":"https://openalex.org/C141071460","wikidata":"https://www.wikidata.org/wiki/Q40821","display_name":"Surgery","level":1,"score":0.11167186498641968},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1117/12.2318464","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2318464","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"14th International Workshop on Breast Imaging (IWBI 2018)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:openaire/5fbfcbfc-4711-489c-885c-979cd4082a35","is_oa":true,"landing_page_url":"https://research.manchester.ac.uk/en/publications/5fbfcbfc-4711-489c-885c-979cd4082a35","pdf_url":null,"source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ionescu, G V, Fergie, M, Berks, M, Harkness, E F, Hulleman, J, Brentnall, A R, Cuzick, J, Evans, D G & Astley, S M 2018, Using a Convolutional Neural Network to Predict Readers' Estimates of Mammographic Density for Breast Cancer Risk Assessment. in Proceedings Volume 10718, 14th International Workshop on Breast Imaging (IWBI 2018). vol. 10718, SPIE - International Society for Optical Engineering. Proceedings . https://doi.org/10.1117/12.2318464","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:qmro.qmul.ac.uk:123456789/63614","is_oa":false,"landing_page_url":"https://qmro.qmul.ac.uk/xmlui/handle/123456789/63614","pdf_url":null,"source":{"id":"https://openalex.org/S4306400530","display_name":"Queen Mary Research Online (Queen Mary University of London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I166337079","host_organization_name":"Queen Mary University of London","host_organization_lineage":["https://openalex.org/I166337079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Proceeding"}],"best_oa_location":{"id":"pmh:oai:pure.atira.dk:openaire/5fbfcbfc-4711-489c-885c-979cd4082a35","is_oa":true,"landing_page_url":"https://research.manchester.ac.uk/en/publications/5fbfcbfc-4711-489c-885c-979cd4082a35","pdf_url":null,"source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ionescu, G V, Fergie, M, Berks, M, Harkness, E F, Hulleman, J, Brentnall, A R, Cuzick, J, Evans, D G & Astley, S M 2018, Using a Convolutional Neural Network to Predict Readers' Estimates of Mammographic Density for Breast Cancer Risk Assessment. in Proceedings Volume 10718, 14th International Workshop on Breast Imaging (IWBI 2018). vol. 10718, SPIE - International Society for Optical Engineering. Proceedings . https://doi.org/10.1117/12.2318464","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.7599999904632568,"id":"https://metadata.un.org/sdg/3"}],"awards":[{"id":"https://openalex.org/G2490867277","display_name":null,"funder_award_id":"RP-PG-0707-10031","funder_id":"https://openalex.org/F4320319990","funder_display_name":"National Institute for Health and Care Research"}],"funders":[{"id":"https://openalex.org/F4320319990","display_name":"National Institute for Health and Care Research","ror":"https://ror.org/0187kwz08"},{"id":"https://openalex.org/F4320333687","display_name":"Manchester Biomedical Research Centre","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1556673366","https://openalex.org/W1627054999","https://openalex.org/W1665214252","https://openalex.org/W1836465849","https://openalex.org/W2065934752","https://openalex.org/W2068611090","https://openalex.org/W2090624356","https://openalex.org/W2102605133","https://openalex.org/W2107253524","https://openalex.org/W2116278301","https://openalex.org/W2124812493","https://openalex.org/W2133750711","https://openalex.org/W2134650356","https://openalex.org/W2160408484","https://openalex.org/W2163605009","https://openalex.org/W2182350758","https://openalex.org/W2240965754","https://openalex.org/W2294923432","https://openalex.org/W2296247486","https://openalex.org/W2338271170","https://openalex.org/W2341106171","https://openalex.org/W2409650203","https://openalex.org/W2499052613","https://openalex.org/W2547496647","https://openalex.org/W2592929672","https://openalex.org/W2754958205","https://openalex.org/W2765989918","https://openalex.org/W2786981970","https://openalex.org/W4250365471","https://openalex.org/W6631190155","https://openalex.org/W6633323296","https://openalex.org/W6636671767","https://openalex.org/W6637242042","https://openalex.org/W6638667902","https://openalex.org/W6667126961","https://openalex.org/W6678494838","https://openalex.org/W6684191040","https://openalex.org/W6689996380","https://openalex.org/W6697325517","https://openalex.org/W6704114496","https://openalex.org/W6714550321","https://openalex.org/W6729204975","https://openalex.org/W6744375314","https://openalex.org/W6777047061","https://openalex.org/W6881968250"],"related_works":["https://openalex.org/W2050777812","https://openalex.org/W1987318790","https://openalex.org/W2489417550","https://openalex.org/W2927354646","https://openalex.org/W2144958057","https://openalex.org/W2778894640","https://openalex.org/W2043525882","https://openalex.org/W2111863209","https://openalex.org/W4362522338","https://openalex.org/W2066808337"],"abstract_inverted_index":{"<strong>Background</strong>:":[0],"Mammographic":[1],"density":[2,16,31,194],"is":[3],"an":[4,101],"important":[5],"risk":[6,26],"factor":[7],"for":[8,100,201,211],"breast":[9],"cancer.":[10],"Recent":[11],"research":[12],"demonstrated":[13,234],"that":[14,91],"percentage":[15,193],"assessed":[17],"visually":[18],"using":[19,66,107],"Visual":[20],"Analogue":[21],"Scales":[22],"(VAS)":[23],"showed":[24],"stronger":[25],"prediction":[27],"than":[28],"existing":[29,241],"automated":[30,44,232],"measures,":[32],"suggesting":[33],"readers":[34],"may":[35],"recognise":[36],"relevant":[37],"image":[38],"features":[39],"not":[40],"yet":[41],"captured":[42],"by":[43],"methods.":[45],"<strong>Method</strong>:":[46],"We":[47],"have":[48],"built":[49],"convolutional":[50],"neural":[51],"networks":[52],"(CNN)":[53],"to":[54,143],"predict":[55,97],"VAS":[56,74,88,98,161],"scores":[57],"from":[58,111],"full-field":[59],"digital":[60],"mammograms.":[61],"The":[62],"CNNs":[63],"are":[64],"trained":[65,106],"whole-image":[67],"mammograms,":[68],"each":[69],"labelled":[70],"with":[71,138,214,240],"the":[72,163,179,187,202,212],"average":[73],"score":[75,89,99],"of":[76,120,126,129,136,184,192,218],"two":[77],"independent":[78],"readers.":[79],"They":[80],"learn":[81],"a":[82,117],"mapping":[83],"between":[84,157],"mammographic":[85,109],"appearance":[86],"and":[87,114,123,133,151,159,170,206,223],"so":[90],"at":[92],"test":[93],"time,":[94],"they":[95],"can":[96],"unseen":[102],"image.":[103],"Networks":[104],"were":[105,195],"67520":[108],"images":[110,122,135],"16968":[112],"women,":[113],"tested":[115],"on":[116,145],"large":[118,164],"dataset":[119,165],"73128":[121],"case-control":[124,180],"sets":[125],"contralateral":[127],"mammograms":[128],"screen":[130,203],"detected":[131,140,204],"cancers":[132,139,205],"prior":[134],"women":[137],"subsequently,":[141],"matched":[142,215],"controls":[144],"age,":[146],"menopausal":[147],"status,":[148],"parity,":[149],"HRT":[150],"BMI.":[152],"<strong>Results</strong>:":[153],"Pearson's":[154],"correlation":[155],"coefficient":[156],"readers'":[158],"predicted":[160],"in":[162,186],"was":[166],"0.79":[167],"per":[168,172],"mammogram":[169],"0.83":[171],"woman":[173],"(averaging":[174],"over":[175],"all":[176],"views).":[177],"In":[178],"sets,":[181],"odds":[182],"ratios":[183],"cancer":[185],"highest":[188],"vs":[189],"lowest":[190],"quintile":[191],"3.07":[196],"(95%CI:":[197],"1.97":[198],"-":[199,209,221,226],"4.77)":[200],"3.52":[207],"(2.22":[208],"5.58)":[210],"priors,":[213],"concordance":[216],"indices":[217],"0.59":[219],"(0.55":[220],"0.64)":[222],"0.61":[224],"(0.58":[225],"0.65)":[227],"respectively.":[228],"<strong>Conclusion</strong>:":[229],"Our":[230],"fully":[231],"method":[233],"encouraging":[235],"results":[236],"which":[237],"compare":[238],"well":[239],"methods,":[242],"including":[243],"VAS.":[244]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2018-07-19T00:00:00"}
