{"id":"https://openalex.org/W2920741869","doi":"https://doi.org/10.1117/12.2513420","title":"Mammographic breast density classification using a deep neural network: assessment on the basis of inter-observer variability","display_name":"Mammographic breast density classification using a deep neural network: assessment on the basis of inter-observer variability","publication_year":2019,"publication_date":"2019-03-04","ids":{"openalex":"https://openalex.org/W2920741869","doi":"https://doi.org/10.1117/12.2513420","mag":"2920741869"},"language":"en","primary_location":{"id":"doi:10.1117/12.2513420","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2513420","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment","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/A5086047990","display_name":"Nico Kaiser","orcid":"https://orcid.org/0000-0002-5644-890X"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]},{"id":"https://openalex.org/I4210153902","display_name":"Siemens Healthineers (Germany)","ror":"https://ror.org/0449c4c15","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210153902"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Nico Kaiser","raw_affiliation_strings":["Friedrich-Alexander-Univ. Erlangen-N\u00fcrnberg (Germany)","Siemens Healthcare GmbH (Germany)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Friedrich-Alexander-Univ. Erlangen-N\u00fcrnberg (Germany)","institution_ids":["https://openalex.org/I181369854"]},{"raw_affiliation_string":"Siemens Healthcare GmbH (Germany)","institution_ids":["https://openalex.org/I4210153902"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030800334","display_name":"Andreas Fieselmann","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153902","display_name":"Siemens Healthineers (Germany)","ror":"https://ror.org/0449c4c15","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210153902"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andreas Fieselmann","raw_affiliation_strings":["Siemens Healthcare GmbH (Germany)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Siemens Healthcare GmbH (Germany)","institution_ids":["https://openalex.org/I4210153902"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049783039","display_name":"Ludwig Ritschl","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153902","display_name":"Siemens Healthineers (Germany)","ror":"https://ror.org/0449c4c15","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210153902"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ludwig Ritschl","raw_affiliation_strings":["Siemens Healthcare GmbH (Germany)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Siemens Healthcare GmbH (Germany)","institution_ids":["https://openalex.org/I4210153902"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015456468","display_name":"S. Kappler","orcid":"https://orcid.org/0000-0001-7223-7312"},"institutions":[{"id":"https://openalex.org/I4210153902","display_name":"Siemens Healthineers (Germany)","ror":"https://ror.org/0449c4c15","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210153902"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Steffen Kappler","raw_affiliation_strings":["Siemens Healthcare GmbH (Germany)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Siemens Healthcare GmbH (Germany)","institution_ids":["https://openalex.org/I4210153902"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073918042","display_name":"Nishant Ravikumar","orcid":"https://orcid.org/0000-0003-0134-107X"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Nishant Ravikumar","raw_affiliation_strings":["Friedrich-Alexander-Univ. Erlangen-N\u00fcrnberg (Germany)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Friedrich-Alexander-Univ. Erlangen-N\u00fcrnberg (Germany)","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010990212","display_name":"Sulaiman Vesal","orcid":"https://orcid.org/0000-0001-6156-9338"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Sulaiman Vesal","raw_affiliation_strings":["Friedrich-Alexander-Univ. Erlangen-N\u00fcrnberg (Germany)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Friedrich-Alexander-Univ. Erlangen-N\u00fcrnberg (Germany)","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109007175","display_name":"Andreas Maier","orcid":null},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andreas Maier","raw_affiliation_strings":["Friedrich-Alexander-Univ. Erlangen-N\u00fcrnberg (Germany)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Friedrich-Alexander-Univ. Erlangen-N\u00fcrnberg (Germany)","institution_ids":["https://openalex.org/I181369854"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.518,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.69007094,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"23","last_page":"23"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11361","display_name":"Digital Radiography and Breast Imaging","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T11361","display_name":"Digital Radiography and Breast Imaging","score":0.9998000264167786,"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/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/T10556","display_name":"Global Cancer Incidence and Screening","score":0.9948999881744385,"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/kappa","display_name":"Kappa","score":0.8173863887786865},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.6559451222419739},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6372179985046387},{"id":"https://openalex.org/keywords/breast-density","display_name":"Breast density","score":0.6281535029411316},{"id":"https://openalex.org/keywords/mammographic-density","display_name":"MAMMOGRAPHIC DENSITY","score":0.6197668313980103},{"id":"https://openalex.org/keywords/mammography","display_name":"Mammography","score":0.5611481070518494},{"id":"https://openalex.org/keywords/cohens-kappa","display_name":"Cohen's kappa","score":0.5274665355682373},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.521743893623352},{"id":"https://openalex.org/keywords/observer","display_name":"Observer (physics)","score":0.5177502036094666},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48294657468795776},{"id":"https://openalex.org/keywords/bi-rads","display_name":"BI-RADS","score":0.4725482165813446},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.42709869146347046},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4063948690891266},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.39452463388442993},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.3518100380897522},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.33667707443237305},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.30230218172073364},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.24648430943489075},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.20190587639808655},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.09886914491653442}],"concepts":[{"id":"https://openalex.org/C2778724333","wikidata":"https://www.wikidata.org/wiki/Q14401","display_name":"Kappa","level":2,"score":0.8173863887786865},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.6559451222419739},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6372179985046387},{"id":"https://openalex.org/C3018951153","wikidata":"https://www.wikidata.org/wiki/Q17011492","display_name":"Breast density","level":5,"score":0.6281535029411316},{"id":"https://openalex.org/C2909213482","wikidata":"https://www.wikidata.org/wiki/Q17011492","display_name":"MAMMOGRAPHIC DENSITY","level":5,"score":0.6197668313980103},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.5611481070518494},{"id":"https://openalex.org/C163864269","wikidata":"https://www.wikidata.org/wiki/Q1107106","display_name":"Cohen's kappa","level":2,"score":0.5274665355682373},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.521743893623352},{"id":"https://openalex.org/C2780704645","wikidata":"https://www.wikidata.org/wiki/Q9251458","display_name":"Observer (physics)","level":2,"score":0.5177502036094666},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48294657468795776},{"id":"https://openalex.org/C2779098232","wikidata":"https://www.wikidata.org/wiki/Q903975","display_name":"BI-RADS","level":5,"score":0.4725482165813446},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.42709869146347046},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4063948690891266},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.39452463388442993},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.3518100380897522},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.33667707443237305},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30230218172073364},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.24648430943489075},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.20190587639808655},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.09886914491653442},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2513420","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2513420","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.7900000214576721,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2079966654","https://openalex.org/W2109063533","https://openalex.org/W2109283698","https://openalex.org/W2117938688","https://openalex.org/W2145872944","https://openalex.org/W2154780170","https://openalex.org/W2494536949","https://openalex.org/W2896522127","https://openalex.org/W2912391947","https://openalex.org/W2964348594","https://openalex.org/W4236735049","https://openalex.org/W4250365471","https://openalex.org/W6674012025","https://openalex.org/W6677047224","https://openalex.org/W6745689145"],"related_works":["https://openalex.org/W2050777812","https://openalex.org/W2927354646","https://openalex.org/W2041704199","https://openalex.org/W2168191810","https://openalex.org/W2621162536","https://openalex.org/W2088738777","https://openalex.org/W2075097563","https://openalex.org/W3093479359","https://openalex.org/W1989829869","https://openalex.org/W2322522942"],"abstract_inverted_index":{"Mammographic":[0],"breast":[1,9,20,110,236],"density":[2,21,43,237],"is":[3,47,160,165,239,245],"an":[4,94,127,216,261],"important":[5],"risk":[6],"marker":[7],"in":[8],"cancer":[10,111],"screening.":[11],"The":[12,103,118,146,172,212],"ACR":[13],"BI-RADS":[14],"guidelines":[15],"(5th":[16],"ed.)":[17],"define":[18],"four":[19,115],"categories":[22,249],"that":[23,232,251],"can":[24,256],"be":[25],"dichotomized":[26],"by":[27,45,49,156],"the":[28,37,41,60,72,78,84,91,143,150,162,200,243],"two":[29,79,144,175,248],"super-classes":[30],"dense\"":[31],"and":[32,65,89,131,224,250],"not":[33],"dense\".":[34],"Due":[35],"to":[36,71,93,122,139,149,184,199,205,218,247,260,268],"qualitative":[38],"description":[39],"of":[40,68,126,142,170,174,182,187,192,197,210,222,227],"categories,":[42],"assessment":[44,96,238],"radiologists":[46,70,88,177,198,221],"characterized":[48],"a":[50,98,134,157,168,180,185,190,208,225],"high":[51,240],"inter-observer":[52,233],"variability.":[53],"To":[54],"quantify":[55],"this":[56],"variability,":[57],"we":[58,82],"compute":[59],"overall":[61],"percentage":[62],"agreement":[63,148],"(OPA)":[64],"Cohen's":[66,194],"kappa":[67,195,226],"32":[69,220],"panel":[73,151],"majority":[74,152],"vote":[75,153],"based":[76],"on":[77,133],"super-classes.":[80,145],"Further,":[81],"analyze":[83],"OPA":[85,173,217],"between":[86],"individual":[87,176],"compare":[90],"performances":[92],"automated":[95,271],"via":[97],"convolutional":[99,253],"neural":[100,254],"network":[101,255],"(CNN).":[102],"data":[104],"used":[105],"for":[106,235,274],"evaluation":[107],"contains":[108],"600":[109],"screening":[112],"examinations":[113],"with":[114,136,167,189,207,270],"views":[116,125],"each.":[117],"CNN":[119],"was":[120],"designed":[121],"take":[123],"all":[124,219],"examination":[128],"as":[129],"input":[130],"trained":[132],"dataset":[135],"7186":[137],"cases":[138],"output":[140],"one":[141],"highest":[147],"(PMV)":[154],"achieved":[155],"single":[158],"radiologist":[159],"99%,":[161],"lowest":[163],"score":[164],"71%":[166],"mean":[169,191,209],"89%.":[171],"ranges":[178],"from":[179,203],"maximum":[181],"97.5%":[183],"minimum":[186],"50.5%":[188],"83%.":[193],"values":[196],"PMV":[201],"range":[202],"0.97":[204],"0.47":[206],"0.77.":[211],"presented":[213],"algorithm":[214],"reaches":[215],"88%":[223],"0.75.":[228],"Our":[229],"results":[230],"show":[231],"variability":[234],"even":[241],"if":[242],"problem":[244],"reduced":[246],"our":[252],"provide":[257],"labelling":[258],"comparable":[259],"average":[262],"radiologist.":[263],"We":[264],"also":[265],"discuss":[266],"how":[267],"deal":[269],"classification":[272],"methods":[273],"subjective":[275],"tasks.":[276]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-04T07:58:01.006859","created_date":"2025-10-10T00:00:00"}