{"id":"https://openalex.org/W2998857049","doi":"https://doi.org/10.1109/bigdataservice49289.2020.00026","title":"Mammography Image BI-RADS Classification Using OHPLall","display_name":"Mammography Image BI-RADS Classification Using OHPLall","publication_year":2020,"publication_date":"2020-08-01","ids":{"openalex":"https://openalex.org/W2998857049","doi":"https://doi.org/10.1109/bigdataservice49289.2020.00026","mag":"2998857049"},"language":"en","primary_location":{"id":"doi:10.1109/bigdataservice49289.2020.00026","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdataservice49289.2020.00026","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService)","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/A5066531003","display_name":"Robert Vanderheyden","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Robert Vanderheyden","raw_affiliation_strings":["IBM, Acworth, GA, USA"],"affiliations":[{"raw_affiliation_string":"IBM, Acworth, GA, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101628906","display_name":"Ying Xie","orcid":"https://orcid.org/0000-0002-6419-3986"},"institutions":[{"id":"https://openalex.org/I172980758","display_name":"Kennesaw State University","ror":"https://ror.org/00jeqjx33","country_code":"US","type":"education","lineage":["https://openalex.org/I172980758"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ying Xie","raw_affiliation_strings":["Dept. of Information Technology, Kennesaw State University, Kennesaw, GA, USA"],"affiliations":[{"raw_affiliation_string":"Dept. of Information Technology, Kennesaw State University, Kennesaw, GA, USA","institution_ids":["https://openalex.org/I172980758"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5066531003"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1371,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.52178829,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":"abs 1811 2661","issue":null,"first_page":"120","last_page":"127"},"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9961000084877014,"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":0.9710000157356262,"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/mammography","display_name":"Mammography","score":0.7539097666740417},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6890672445297241},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6534215211868286},{"id":"https://openalex.org/keywords/bi-rads","display_name":"BI-RADS","score":0.6343265771865845},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5927821397781372},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5588407516479492},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5324740409851074},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.48776012659072876},{"id":"https://openalex.org/keywords/breast-imaging","display_name":"Breast imaging","score":0.474735826253891},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.456645667552948},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.4507647454738617},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3890329897403717},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2512456178665161},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.16712862253189087},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.12928727269172668},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.08554467558860779}],"concepts":[{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.7539097666740417},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6890672445297241},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6534215211868286},{"id":"https://openalex.org/C2779098232","wikidata":"https://www.wikidata.org/wiki/Q903975","display_name":"BI-RADS","level":5,"score":0.6343265771865845},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5927821397781372},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5588407516479492},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5324740409851074},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.48776012659072876},{"id":"https://openalex.org/C2777432617","wikidata":"https://www.wikidata.org/wiki/Q22905905","display_name":"Breast imaging","level":5,"score":0.474735826253891},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.456645667552948},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4507647454738617},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3890329897403717},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2512456178665161},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.16712862253189087},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.12928727269172668},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.08554467558860779},{"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/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdataservice49289.2020.00026","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdataservice49289.2020.00026","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService)","raw_type":"proceedings-article"},{"id":"pmh:oai:digitalcommons.kennesaw.edu:ohplpublications-1003","is_oa":false,"landing_page_url":"https://digitalcommons.kennesaw.edu/ohplpublications/2","pdf_url":null,"source":{"id":"https://openalex.org/S4377196456","display_name":"DigitalCommons - Kennesaw State University (Kennesaw State University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I172980758","host_organization_name":"Kennesaw State University","host_organization_lineage":["https://openalex.org/I172980758"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"OHPL Publications","raw_type":"text"}],"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/W2083927153","https://openalex.org/W2131490903","https://openalex.org/W2776937175","https://openalex.org/W2899593323","https://openalex.org/W2907066882","https://openalex.org/W2914173981","https://openalex.org/W2963446712","https://openalex.org/W6755842985","https://openalex.org/W6948127220"],"related_works":["https://openalex.org/W2330711938","https://openalex.org/W4224140527","https://openalex.org/W2086543716","https://openalex.org/W2154027915","https://openalex.org/W3012175946","https://openalex.org/W3092582177","https://openalex.org/W4396722336","https://openalex.org/W2109305028","https://openalex.org/W2013609118","https://openalex.org/W2102519580"],"abstract_inverted_index":{"Medical":[0],"image":[1,22,93,113],"analysis":[2],"and":[3,61,160],"classification,":[4],"using":[5,164],"machine":[6],"learning,":[7],"particularly":[8],"Convolutional":[9],"Neural":[10],"Networks,":[11],"have":[12],"demonstrated":[13,154],"a":[14,53,70,83,98,120,168],"great":[15,161],"deal":[16],"of":[17,49,73,82,89,97,145,163],"success.":[18],"Research":[19],"into":[20],"mammography":[21,50,149],"classification":[23,37,48],"tended":[24],"to":[25,78,142],"focus":[26],"on":[27,86,112,148],"either":[28],"binary":[29],"outcome":[30],"(malignancy":[31],"or":[32,34],"benign)":[33],"nominal":[35],"(unordered)":[36],"for":[38,46,137],"multiclass":[39],"labels":[40],"[1].":[41],"The":[42,95],"industry":[43],"standard":[44],"metric":[45],"radiologist's":[47],"images":[51],"is":[52],"rating":[54],"scale":[55],"called":[56,125],"BI-RADS":[57,102,146],"(Breast":[58],"Imaging":[59],"Reporting":[60],"Data":[62],"System),":[63],"where":[64],"values":[65],"1":[66],"through":[67],"5":[68],"are":[69,76],"distinct":[71],"progression":[72],"assessment":[74],"that":[75,100],"intended":[77],"denote":[79],"higher":[80],"risk":[81],"malignancy,":[84],"based":[85],"the":[87,143],"characteristics":[88],"anomalies":[90],"within":[91],"an":[92,108],"[1][2][3].":[94],"development":[96],"classifier":[99],"predicts":[101],"1-5,":[103],"would":[104],"provide":[105],"radiologists":[106],"with":[107,139],"objective":[109],"second":[110],"opinion":[111],"anomalies.":[114],"In":[115],"this":[116],"paper,":[117],"we":[118],"applied":[119],"novel":[121],"Deep":[122],"Learning":[123],"method":[124],"OHPLall":[126,159,165],"(Ordinal":[127],"Hyperplane":[128],"Loss":[129],"-":[130],"all":[131],"centroids),":[132],"which":[133],"was":[134],"specifically":[135],"designed":[136],"data":[138],"ordinal":[140],"classes,":[141],"predictions":[144],"scales":[147],"images.":[150],"Our":[151],"experimental":[152],"study":[153],"promising":[155],"results":[156],"generated":[157],"by":[158],"potential":[162],"models":[166],"as":[167],"supplemental":[169],"diagnostic":[170],"tool.":[171]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}