{"id":"https://openalex.org/W3002692353","doi":"https://doi.org/10.1109/cisp-bmei48845.2019.8966022","title":"Nipple Detection in Mammogram Using a New Convolutional Neural Network Architecture","display_name":"Nipple Detection in Mammogram Using a New Convolutional Neural Network Architecture","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W3002692353","doi":"https://doi.org/10.1109/cisp-bmei48845.2019.8966022","mag":"3002692353"},"language":"en","primary_location":{"id":"doi:10.1109/cisp-bmei48845.2019.8966022","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei48845.2019.8966022","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","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/A5036652776","display_name":"Yuyang Lin","orcid":"https://orcid.org/0000-0002-3585-7819"},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuyang Lin","raw_affiliation_strings":["Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China","institution_ids":["https://openalex.org/I69356397"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100783920","display_name":"Muyang Li","orcid":"https://orcid.org/0000-0003-1009-2343"},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Muyang Li","raw_affiliation_strings":["Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China","institution_ids":["https://openalex.org/I69356397"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101900248","display_name":"Sirui Chen","orcid":"https://orcid.org/0000-0001-9918-6850"},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sirui Chen","raw_affiliation_strings":["Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China","institution_ids":["https://openalex.org/I69356397"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032994993","display_name":"Limin Yu","orcid":"https://orcid.org/0000-0002-6891-0604"},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Limin Yu","raw_affiliation_strings":["Electrical and Electronic Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"Electrical and Electronic Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, China","institution_ids":["https://openalex.org/I69356397"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062318925","display_name":"Fei Ma","orcid":"https://orcid.org/0000-0001-6099-480X"},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Ma","raw_affiliation_strings":["Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China","institution_ids":["https://openalex.org/I69356397"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5036652776"],"corresponding_institution_ids":["https://openalex.org/I69356397"],"apc_list":null,"apc_paid":null,"fwci":0.14,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.59816884,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9998000264167786,"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":0.9998000264167786,"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/T11448","display_name":"Face recognition and analysis","score":0.9742000102996826,"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"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9740999937057495,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7596005201339722},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7048581838607788},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6998569369316101},{"id":"https://openalex.org/keywords/mammography","display_name":"Mammography","score":0.6852630376815796},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.6417822241783142},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5396584868431091},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49379095435142517},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.49232399463653564},{"id":"https://openalex.org/keywords/landmark","display_name":"Landmark","score":0.46170029044151306},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4431115984916687},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4406469464302063},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.38224324584007263},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.14750468730926514},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.1416727900505066},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1386854648590088}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7596005201339722},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7048581838607788},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6998569369316101},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.6852630376815796},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.6417822241783142},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5396584868431091},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49379095435142517},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.49232399463653564},{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.46170029044151306},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4431115984916687},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4406469464302063},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.38224324584007263},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.14750468730926514},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.1416727900505066},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1386854648590088},{"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/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cisp-bmei48845.2019.8966022","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei48845.2019.8966022","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6899999976158142,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W41027960","https://openalex.org/W177004468","https://openalex.org/W402813564","https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1553719179","https://openalex.org/W1836465849","https://openalex.org/W1975855047","https://openalex.org/W1996941612","https://openalex.org/W2015278915","https://openalex.org/W2036929809","https://openalex.org/W2051331995","https://openalex.org/W2054162039","https://openalex.org/W2065711422","https://openalex.org/W2097076337","https://openalex.org/W2112971960","https://openalex.org/W2171689646","https://openalex.org/W2172152341","https://openalex.org/W2306541384","https://openalex.org/W2502312327","https://openalex.org/W2525974879","https://openalex.org/W2526653212","https://openalex.org/W2592929672","https://openalex.org/W2613718673","https://openalex.org/W2803808038","https://openalex.org/W2884366600","https://openalex.org/W2889646458","https://openalex.org/W2919115771","https://openalex.org/W2939411050","https://openalex.org/W2949117887","https://openalex.org/W2955721390","https://openalex.org/W2962839335","https://openalex.org/W2963037989","https://openalex.org/W6607184829","https://openalex.org/W6751546485"],"related_works":["https://openalex.org/W2056853153","https://openalex.org/W2057559274","https://openalex.org/W2026924879","https://openalex.org/W2005087563","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Mammogram":[0],"is":[1,26,46,135],"an":[2,10,219],"X-ray":[3],"image":[4],"of":[5,33,41,52,69,119,142,187,195,224],"the":[6,14,47,65,97,100,173,180,185,189,192],"breast.":[7],"It":[8],"plays":[9],"important":[11,67],"role":[12],"in":[13,81,176],"breast":[15,29,90],"cancer":[16,30],"early":[17],"diagnosis.":[18],"In":[19,127,197],"recent":[20,82],"years,":[21],"computer":[22],"aided":[23],"detection":[24,63,222],"(CAD)":[25],"used":[27,94,154,163],"for":[28,155,211],"detection.":[31,43],"Multi-view":[32],"mammograms":[34],"are":[35,93,153,162,170,205],"needed":[36],"to":[37,78,95,110,125,137,164,172,178],"achieve":[38],"high":[39],"accuracy":[40,223],"automatic":[42],"Since":[44],"nipple":[45,62,80,98,221],"only":[48],"landmark":[49],"on":[50,99,140,184],"mammogram":[51,141],"different":[53,76,123],"views":[54,108],"(mediolateral":[55],"oblique":[56],"(MLO)":[57],"and":[58,89,106,114,116,145,150,202],"craniocaudal":[59],"(CC)":[60],"views),":[61],"becomes":[64],"first":[66],"step":[68],"many":[70],"CAD":[71],"systems.":[72],"Researchers":[73],"have":[74],"developed":[75],"models":[77],"detect":[79],"20":[83],"years.":[84],"Grey":[85],"scale,":[86],"geometric":[87],"feature":[88],"edge's":[91],"gradient":[92],"find":[96],"mammogram.":[101],"For":[102],"most":[103],"methods,":[104],"MLO":[105,144],"CC":[107,146],"need":[109,122],"be":[111],"tested":[112],"separately,":[113],"obvious":[115,149],"subtle":[117,151],"types":[118,152],"nipples":[120,139],"also":[121],"methods":[124],"detect.":[126],"this":[128,198],"paper,":[129],"a":[130],"model":[131,175,190],"with":[132],"deep":[133],"learning":[134],"designed":[136],"locate":[138],"both":[143],"views.":[147],"Both":[148],"experiment.":[156],"Four":[157],"convolutional":[158],"neural":[159],"network":[160],"blocks":[161],"attain":[165],"candidate":[166],"blocks.":[167],"Normalization":[168],"layers":[169],"added":[171],"proposed":[174,216],"order":[177],"improve":[179],"domain":[181],"adaptation.":[182],"Based":[183],"intersection":[186],"candidates,":[188],"computes":[191],"final":[193],"block":[194],"nipple.":[196],"experiment,":[199],"train":[200],"set":[201,204],"test":[203],"randomly":[206],"attained":[207],"from":[208],"Digital":[209],"Database":[210],"Screening":[212],"Mammography":[213],"(DDSM).":[214],"Our":[215],"method":[217],"achieved":[218],"overall":[220],"98.00":[225],"<sup":[226],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[227],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">%</sup>":[228],",":[229],"which":[230],"outperformed":[231],"three":[232],"comparative":[233],"methods.":[234]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
