{"id":"https://openalex.org/W2900645962","doi":"https://doi.org/10.1109/iccabs.2018.8542071","title":"Convolutional Neural Network for Automated Mass Segmentation in Mammography","display_name":"Convolutional Neural Network for Automated Mass Segmentation in Mammography","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2900645962","doi":"https://doi.org/10.1109/iccabs.2018.8542071","mag":"2900645962"},"language":"en","primary_location":{"id":"doi:10.1109/iccabs.2018.8542071","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccabs.2018.8542071","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 8th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)","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/A5084096569","display_name":"Dina Abdelhafiz","orcid":"https://orcid.org/0000-0003-3195-6577"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Dina Abdelhafiz","raw_affiliation_strings":["Computer Science and Engineering, University of Connecticut, Storrs, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, University of Connecticut, Storrs, CT, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010254642","display_name":"Sheida Nabavi","orcid":"https://orcid.org/0000-0002-5996-1020"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sheida Nabavi","raw_affiliation_strings":["Computer Science and Engineering, University of Connecticut, Storrs, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, University of Connecticut, Storrs, CT, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010317923","display_name":"Reda A. Ammar","orcid":"https://orcid.org/0000-0003-1332-2677"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Reda Ammar","raw_affiliation_strings":["Computer Science and Engineering, University of Connecticut, Storrs, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, University of Connecticut, Storrs, CT, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070072025","display_name":"Clifford Yang","orcid":"https://orcid.org/0000-0001-7108-2021"},"institutions":[{"id":"https://openalex.org/I75929689","display_name":"UConn Health","ror":"https://ror.org/02kzs4y22","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I75929689"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Clifford Yang","raw_affiliation_strings":["Diagnostic Imaging, University of Connecticut Health Center, Farmington, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Diagnostic Imaging, University of Connecticut Health Center, Farmington, CT, USA","institution_ids":["https://openalex.org/I75929689"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051393532","display_name":"Jinbo Bi","orcid":"https://orcid.org/0000-0001-6996-4092"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jinbo Bi","raw_affiliation_strings":["Computer Science and Engineering, University of Connecticut, Storrs, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, University of Connecticut, Storrs, CT, USA","institution_ids":["https://openalex.org/I140172145"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5084096569"],"corresponding_institution_ids":["https://openalex.org/I140172145"],"apc_list":null,"apc_paid":null,"fwci":0.5081,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.74872353,"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":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":1.0,"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":1.0,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9997000098228455,"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"}},{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.993399977684021,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7897936105728149},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7720006704330444},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7365798950195312},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7253337502479553},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6212030053138733},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.594634473323822},{"id":"https://openalex.org/keywords/mammography","display_name":"Mammography","score":0.589799165725708},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.5881616473197937},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5805231928825378},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.5712392330169678},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5170767307281494},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4918797016143799},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.43661829829216003},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.16523247957229614},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.09855061769485474},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.06977447867393494}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7897936105728149},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7720006704330444},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7365798950195312},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7253337502479553},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6212030053138733},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.594634473323822},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.589799165725708},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.5881616473197937},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5805231928825378},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.5712392330169678},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5170767307281494},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4918797016143799},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43661829829216003},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.16523247957229614},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.09855061769485474},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.06977447867393494},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"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/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.0},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccabs.2018.8542071","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccabs.2018.8542071","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 8th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W749750490","https://openalex.org/W1901129140","https://openalex.org/W2163605009","https://openalex.org/W2493683088","https://openalex.org/W2770372267","https://openalex.org/W2804383999","https://openalex.org/W2964189045","https://openalex.org/W6639824700","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W3082178636","https://openalex.org/W2782041652","https://openalex.org/W2612657834","https://openalex.org/W2392157706","https://openalex.org/W2591697403","https://openalex.org/W2599192953","https://openalex.org/W2952088488","https://openalex.org/W1521968289","https://openalex.org/W4225691210","https://openalex.org/W4399568863"],"abstract_inverted_index":{"Automatic":[0],"segmentation":[1,95,113,273],"and":[2,39,85,120,192,194,230,257,270,291,309,333,348,444],"localization":[3],"of":[4,70,97,159,171,200,225,266,313,321,326,377,388,459,464,476],"lesions":[5,41,122,224,390,447],"in":[6,42,61,103,117,136,361,375,391,425,429,432],"mammogram":[7],"(MG)":[8],"images":[9,64,162,196,220,240,269,304],"are":[10,244,275],"challenging":[11],"problems":[12],"even":[13],"with":[14,324],"employing":[15],"advanced":[16],"methods":[17,23,134,183],"such":[18,78,148],"as":[19,79],"deep":[20],"learning":[21,172],"(DL)":[22],"[1]-[3].":[24],"To":[25,146],"address":[26],"these":[27,153],"challenges,":[28],"we":[29,284],"propose":[30],"to":[31,36,74,108,180,205,277,300,305,366,415,439,462,483,492],"use":[32],"a":[33,93,174],"U-Net":[34,45,72,90,168,209,214,281,344,355,383,410],"approach":[35],"automatically":[37],"detect":[38],"segment":[40],"MG":[43,101,161,195,219,239,268,303,351,393],"images.":[44,352,394],"[4]":[46],"is":[47,138,169,216],"an":[48,98,104,430,454],"end-to-end":[49],"convolutional":[50],"neural":[51],"network":[52],"(CNN)":[53],"based":[54],"model":[55,73,91,210,323,331,338,356,370,384,452,480],"that":[56,131,221],"has":[57,421],"achieved":[58],"remarkable":[59],"results":[60],"segmenting":[62],"bio-medical":[63],"[5].":[65,165],"We":[66,185,340],"modified":[67],"the":[68,71,125,139,198,207,237,255,279,286,292,301,311,314,319,327,334,367,381,389,392,397,405,408,416,433,442,445,450,467,474,477],"architecture":[69],"maximize":[75],"its":[76,109,307,485],"precision":[77],"using":[80,346],"batch":[81],"normalization,":[82],"adding":[83],"dropout,":[84],"data":[86],"augmentations.":[87],"The":[88,128,212,353],"proposed":[89,208,213,280,343,354,382,409,451,478],"predicts":[92],"pixel-wise":[94,112],"map":[96],"input":[99],"full":[100],"image":[102],"efficient":[105],"way":[106],"due":[107],"architecture.":[110],"These":[111],"maps":[114,251],"help":[115],"radiologists":[116],"differentiating":[118],"benign":[119],"malignant":[121],"depend":[123],"on":[124,218],"lesion":[126,259],"shapes.":[127],"main":[129],"challenge":[130],"most":[132],"DL":[133,149,182,479],"face":[135],"mammography":[137],"need":[140,155],"for":[141,261,489],"large":[142],"annotated":[143],"training":[144,160,177,238,302],"data-sets.":[145],"train":[147,206,278],"networks":[150,154],"without":[151],"over-fitting,":[152],"thousands":[156],"or":[157],"millions":[158],"[1],":[163],"[3],":[164],"In":[166,379,395,472],"contrast,":[167,396],"capable":[170],"from":[173,197,437,466],"relatively":[175],"small":[176],"data-set":[178],"compared":[179,318,365,414,461],"other":[181],"[4].":[184],"used":[186,276],"publicly":[187],"available":[188],"databases,":[189],"(CBIS-DDSM,":[190],"BCDR-01,":[191],"INbreast),":[193],"University":[199],"Connecticut":[201],"Health":[202],"Center":[203],"(UCHC)":[204],"[3].":[211],"method":[215,345,400,411],"trained":[217],"have":[222],"mass":[223,234],"different":[226],"sizes,":[227],"shapes,":[228],"margins,":[229],"intensity":[231],"variation":[232],"around":[233],"boundaries.":[235],"All":[236],"containing":[241],"suspicious":[242],"areas":[243],"accompanied":[245],"by":[246],"associated":[247],"pixel-level":[248],"ground":[249],"truth":[250],"(GTMs)":[252],"which":[253],"indicate":[254],"background":[256],"breast":[258],"labels":[260],"each":[262],"pixel.":[263],"A":[264],"total":[265],"2066":[267],"their":[271],"corresponding":[272],"GTMs":[274,443],"model.":[282,418,471],"Moreover,":[283,407],"applied":[285],"adaptive":[287,295],"median":[288],"filter":[289,299],"(AMF)":[290],"contrast":[293],"limited":[294],"histogram":[296],"equalization":[297],"(CLAHE)":[298],"enhance":[306],"characteristics":[308],"improve":[310],"performance":[312,360,475],"downstream":[315],"analysis":[316],"[3].We":[317],"efficiency":[320],"our":[322,342,426],"those":[325],"state-of-the-art":[328,468],"Faster":[329,368,398,469],"R-CNN":[330,369,399,470],"[6]":[332],"region":[335],"growing":[336],"(RG)":[337],"[7].":[339],"tested":[341],"film-based":[347],"fully":[349],"digitized":[350],"shows":[357],"slightly":[358],"better":[359],"detecting":[362],"true":[363],"segments":[364,387],"but":[371],"outperforms":[372],"it":[373],"significantly":[374],"term":[376],"runtime.":[378],"addition,":[380],"gives":[385,401],"precise":[386],"bounding":[402],"boxes":[403],"surrounding":[404],"lesions.":[406],"performs":[412],"superior":[413],"RG":[417],"Data":[419],"augmentation":[420],"been":[422],"very":[423],"effective":[424],"experiments,":[427],"resulting":[428],"increase":[431],"Dice":[434],"similarity":[435],"coefficient":[436],"0.918":[438],"0.983,":[440],"between":[441],"segmented":[446],"maps.":[448],"Also,":[449],"yielded":[453],"Intersection":[455],"over":[456],"Union":[457],"(IoU)":[458],"0.974":[460],"IoU":[463],"0.966":[465],"conclusion,":[473],"show":[481],"promises":[482],"make":[484],"practical":[486],"application":[487],"possible":[488],"clinical":[490],"applications":[491],"assist":[493],"radiologists.":[494]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-05-03T08:25:01.440150","created_date":"2025-10-10T00:00:00"}