{"id":"https://openalex.org/W2304298711","doi":"https://doi.org/10.1109/tmi.2016.2521442","title":"Multiple-Instance Learning for Anomaly Detection in Digital Mammography","display_name":"Multiple-Instance Learning for Anomaly Detection in Digital Mammography","publication_year":2016,"publication_date":"2016-01-25","ids":{"openalex":"https://openalex.org/W2304298711","doi":"https://doi.org/10.1109/tmi.2016.2521442","mag":"2304298711"},"language":"en","primary_location":{"id":"doi:10.1109/tmi.2016.2521442","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2016.2521442","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-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/A5028479581","display_name":"Gwenol\u00e9 Quellec","orcid":"https://orcid.org/0000-0003-1669-7140"},"institutions":[{"id":"https://openalex.org/I154526488","display_name":"Inserm","ror":"https://ror.org/02vjkv261","country_code":"FR","type":"government","lineage":["https://openalex.org/I154526488"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Gwenole Quellec","raw_affiliation_strings":["Inserm, UMR 1101, Brest, France"],"affiliations":[{"raw_affiliation_string":"Inserm, UMR 1101, Brest, France","institution_ids":["https://openalex.org/I154526488"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052923096","display_name":"Mathieu Lamard","orcid":"https://orcid.org/0000-0002-6309-6156"},"institutions":[{"id":"https://openalex.org/I154526488","display_name":"Inserm","ror":"https://ror.org/02vjkv261","country_code":"FR","type":"government","lineage":["https://openalex.org/I154526488"]},{"id":"https://openalex.org/I161929037","display_name":"Universit\u00e9 de Bretagne Occidentale","ror":"https://ror.org/01b8h3982","country_code":"FR","type":"education","lineage":["https://openalex.org/I161929037"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Mathieu Lamard","raw_affiliation_strings":["Inserm, UMR 1101, Brest, France","Univ Bretagne Occidentale, Brest, France"],"affiliations":[{"raw_affiliation_string":"Inserm, UMR 1101, Brest, France","institution_ids":["https://openalex.org/I154526488"]},{"raw_affiliation_string":"Univ Bretagne Occidentale, Brest, France","institution_ids":["https://openalex.org/I161929037"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042156113","display_name":"Michel Cozic","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michel Cozic","raw_affiliation_strings":["Medecom, Plougastel-Daoulas, France"],"affiliations":[{"raw_affiliation_string":"Medecom, Plougastel-Daoulas, France","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062289182","display_name":"Gouenou Coatrieux","orcid":"https://orcid.org/0000-0002-5643-0224"},"institutions":[{"id":"https://openalex.org/I4210126835","display_name":"Universidad Evang\u00e9lica Boliviana","ror":"https://ror.org/036f1as09","country_code":"BO","type":"education","lineage":["https://openalex.org/I4210126835"]},{"id":"https://openalex.org/I154526488","display_name":"Inserm","ror":"https://ror.org/02vjkv261","country_code":"FR","type":"government","lineage":["https://openalex.org/I154526488"]},{"id":"https://openalex.org/I205703379","display_name":"Institut Mines-T\u00e9l\u00e9com","ror":"https://ror.org/025vp2923","country_code":"FR","type":"facility","lineage":["https://openalex.org/I205703379"]}],"countries":["BO","FR"],"is_corresponding":false,"raw_author_name":"Gouenou Coatrieux","raw_affiliation_strings":["INSTITUT TELECOM; TELECOM Bretagne; UEB;, Dpt ITI, Brest, France","Inserm, UMR 1101, Brest, France","INSTITUT TELECOM","TELECOM Bretagne",", Dpt ITI, Brest, France","UEB"],"affiliations":[{"raw_affiliation_string":"INSTITUT TELECOM; TELECOM Bretagne; UEB;, Dpt ITI, Brest, France","institution_ids":["https://openalex.org/I205703379"]},{"raw_affiliation_string":"Inserm, UMR 1101, Brest, France","institution_ids":["https://openalex.org/I154526488"]},{"raw_affiliation_string":"INSTITUT TELECOM","institution_ids":["https://openalex.org/I205703379"]},{"raw_affiliation_string":"TELECOM Bretagne","institution_ids":[]},{"raw_affiliation_string":", Dpt ITI, Brest, France","institution_ids":[]},{"raw_affiliation_string":"UEB","institution_ids":["https://openalex.org/I4210126835"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016278870","display_name":"Guy Cazuguel","orcid":null},"institutions":[{"id":"https://openalex.org/I4210126835","display_name":"Universidad Evang\u00e9lica Boliviana","ror":"https://ror.org/036f1as09","country_code":"BO","type":"education","lineage":["https://openalex.org/I4210126835"]},{"id":"https://openalex.org/I205703379","display_name":"Institut Mines-T\u00e9l\u00e9com","ror":"https://ror.org/025vp2923","country_code":"FR","type":"facility","lineage":["https://openalex.org/I205703379"]},{"id":"https://openalex.org/I154526488","display_name":"Inserm","ror":"https://ror.org/02vjkv261","country_code":"FR","type":"government","lineage":["https://openalex.org/I154526488"]}],"countries":["BO","FR"],"is_corresponding":false,"raw_author_name":"Guy Cazuguel","raw_affiliation_strings":["INSTITUT TELECOM; TELECOM Bretagne; UEB;, Dpt ITI, Brest, France","Inserm, UMR 1101, Brest, France",", Dpt ITI, Brest, France","UEB","INSTITUT TELECOM","TELECOM Bretagne"],"affiliations":[{"raw_affiliation_string":"INSTITUT TELECOM; TELECOM Bretagne; UEB;, Dpt ITI, Brest, France","institution_ids":["https://openalex.org/I205703379"]},{"raw_affiliation_string":"Inserm, UMR 1101, Brest, France","institution_ids":["https://openalex.org/I154526488"]},{"raw_affiliation_string":", Dpt ITI, Brest, France","institution_ids":[]},{"raw_affiliation_string":"UEB","institution_ids":["https://openalex.org/I4210126835"]},{"raw_affiliation_string":"INSTITUT TELECOM","institution_ids":["https://openalex.org/I205703379"]},{"raw_affiliation_string":"TELECOM Bretagne","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5028479581"],"corresponding_institution_ids":["https://openalex.org/I154526488"],"apc_list":null,"apc_paid":null,"fwci":11.1532,"has_fulltext":false,"cited_by_count":82,"citation_normalized_percentile":{"value":0.98323102,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"35","issue":"7","first_page":"1604","last_page":"1614"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9968000054359436,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7561327219009399},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7464354634284973},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7199802398681641},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7049349546432495},{"id":"https://openalex.org/keywords/mammography","display_name":"Mammography","score":0.6651505827903748},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.6048275232315063},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6007150411605835},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5666134357452393},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5118164420127869},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.43137598037719727},{"id":"https://openalex.org/keywords/digital-mammography","display_name":"Digital mammography","score":0.4206767678260803},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3779587745666504},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.33878475427627563},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.16747912764549255},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.11854073405265808}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7561327219009399},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7464354634284973},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7199802398681641},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7049349546432495},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.6651505827903748},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.6048275232315063},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6007150411605835},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5666134357452393},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5118164420127869},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.43137598037719727},{"id":"https://openalex.org/C2781281974","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Digital mammography","level":5,"score":0.4206767678260803},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3779587745666504},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.33878475427627563},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.16747912764549255},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.11854073405265808},{"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","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":2,"locations":[{"id":"doi:10.1109/tmi.2016.2521442","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2016.2521442","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},{"id":"mag:26829783","is_oa":false,"landing_page_url":"https://www.airitilibrary.com/Publication/Index/16726154-200907-200910190001-200910190001-122-124","pdf_url":null,"source":{"id":"https://openalex.org/S4306554234","display_name":"\u6e56\u5357\u6587\u7406\u5b66\u9662\u5b66\u62a5\uff1a\u793e\u4f1a\u79d1\u5b66\u7248","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"\u6e56\u5357\u6587\u7406\u5b66\u9662\u5b66\u62a5\uff1a\u793e\u4f1a\u79d1\u5b66\u7248","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.44999998807907104,"display_name":"Good health and well-being"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320327215","display_name":"R\u00e9gion Bretagne","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":79,"referenced_works":["https://openalex.org/W59164597","https://openalex.org/W101697616","https://openalex.org/W177004468","https://openalex.org/W1261214144","https://openalex.org/W1501799430","https://openalex.org/W1971917403","https://openalex.org/W1973351628","https://openalex.org/W1974312253","https://openalex.org/W1974883251","https://openalex.org/W1977579905","https://openalex.org/W1978639885","https://openalex.org/W1979010656","https://openalex.org/W1983659238","https://openalex.org/W1985858131","https://openalex.org/W1991391560","https://openalex.org/W1993288162","https://openalex.org/W1993584845","https://openalex.org/W1994899130","https://openalex.org/W1998162403","https://openalex.org/W2003304826","https://openalex.org/W2003367354","https://openalex.org/W2004031835","https://openalex.org/W2009888820","https://openalex.org/W2010792435","https://openalex.org/W2015103117","https://openalex.org/W2030814277","https://openalex.org/W2034823431","https://openalex.org/W2036424610","https://openalex.org/W2039051707","https://openalex.org/W2044465660","https://openalex.org/W2050997943","https://openalex.org/W2051022335","https://openalex.org/W2054209169","https://openalex.org/W2056904197","https://openalex.org/W2062418426","https://openalex.org/W2065814655","https://openalex.org/W2069568152","https://openalex.org/W2071690793","https://openalex.org/W2076360002","https://openalex.org/W2078579128","https://openalex.org/W2080319033","https://openalex.org/W2094992123","https://openalex.org/W2098140880","https://openalex.org/W2098451201","https://openalex.org/W2102363288","https://openalex.org/W2106596998","https://openalex.org/W2108745803","https://openalex.org/W2109722393","https://openalex.org/W2110119381","https://openalex.org/W2112270781","https://openalex.org/W2113180829","https://openalex.org/W2119821739","https://openalex.org/W2120580182","https://openalex.org/W2121782222","https://openalex.org/W2127787047","https://openalex.org/W2131617873","https://openalex.org/W2133288557","https://openalex.org/W2134560790","https://openalex.org/W2142730785","https://openalex.org/W2145029737","https://openalex.org/W2145802391","https://openalex.org/W2154318594","https://openalex.org/W2156877470","https://openalex.org/W2163973672","https://openalex.org/W2165220614","https://openalex.org/W2165731505","https://openalex.org/W2166010828","https://openalex.org/W2171723438","https://openalex.org/W2236960602","https://openalex.org/W2337904331","https://openalex.org/W2519505616","https://openalex.org/W4239510810","https://openalex.org/W6602403231","https://openalex.org/W6607184829","https://openalex.org/W6669992580","https://openalex.org/W6676245398","https://openalex.org/W6683033130","https://openalex.org/W6704028418","https://openalex.org/W6726705939"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W2144299087","https://openalex.org/W2557931800","https://openalex.org/W2116047071","https://openalex.org/W1993531705","https://openalex.org/W54977395","https://openalex.org/W3197491245","https://openalex.org/W1966034269","https://openalex.org/W3210364259","https://openalex.org/W2000845075"],"abstract_inverted_index":{"This":[0,198],"paper":[1],"describes":[2],"a":[3,116,144,149],"computer-aided":[4],"detection":[5,61],"and":[6,65,77,154,172],"diagnosis":[7,102],"system":[8,23],"for":[9,35,215],"breast":[10],"cancer,":[11],"the":[12,26,46,60,96,152,176,181,190,213],"most":[13],"common":[14],"form":[15],"of":[16,62,121],"cancer":[17],"among":[18],"women,":[19],"using":[20,164],"mammography.":[21],"The":[22],"relies":[24],"on":[25,175,207],"Multiple-Instance":[27],"Learning":[28],"(MIL)":[29],"paradigm,":[30],"which":[31,184],"has":[32],"proven":[33],"useful":[34],"medical":[36,209],"decision":[37],"support":[38],"in":[39,79],"previous":[40],"works":[41],"from":[42,59,74],"our":[43],"team.":[44],"In":[45,115,148],"proposed":[47],"framework,":[48],"breasts":[49],"are":[50,72,108,123,140,158],"first":[51,117,191],"partitioned":[52],"adaptively":[53],"into":[54,143],"regions.":[55],"Then,":[56],"features":[57],"derived":[58],"lesions":[63,122],"(masses":[64],"microcalcifications)":[66],"as":[67,69,85],"well":[68],"textural":[70],"features,":[71],"extracted":[73],"each":[75,135],"region":[76],"combined":[78,142],"order":[80],"to":[81,110,125,134],"classify":[82],"mammography":[83],"examinations":[84],"\u201cnormal\u201d":[86],"or":[87],"\u201cabnormal\u201d.":[88],"Whenever":[89],"an":[90,127,131],"abnormal":[91],"examination":[92],"record":[93],"is":[94,185,196],"detected,":[95],"regions":[97],"that":[98,100,129,180,200],"induced":[99],"automated":[101],"can":[103,203],"be":[104,204],"highlighted.":[105],"Two":[106],"strategies":[107],"evaluated":[109],"define":[111],"this":[112],"anomaly":[113,132,138,146,156,201],"detector.":[114],"scenario,":[118,151],"manual":[119,162,216],"segmentations":[120],"used":[124],"train":[126],"SVM":[128],"assigns":[130],"index":[133],"region;":[136],"local":[137,153],"indices":[139],"then":[141],"global":[145,155],"index.":[147],"second":[150,182],"detectors":[157,202],"trained":[159,206],"simultaneously,":[160],"without":[161,212],"segmentations,":[163],"various":[165],"MIL":[166],"algorithms":[167],"(DD,":[168],"APR,":[169],"mi-SVM,":[170],"MI-SVM":[171],"MILBoost).":[173],"Experiments":[174],"DDSM":[177],"dataset":[178],"show":[179],"approach,":[183,192],"only":[186],"weakly-supervised,":[187],"surprisingly":[188],"outperforms":[189],"even":[193],"though":[194],"it":[195],"strongly-supervised.":[197],"suggests":[199],"advantageously":[205],"large":[208],"image":[210],"archives,":[211],"need":[214],"segmentation.":[217]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":15},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":11},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":7},{"year":2016,"cited_by_count":1}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2016-06-24T00:00:00"}
