{"id":"https://openalex.org/W4285117207","doi":"https://doi.org/10.1109/access.2022.3184453","title":"Heatmap Assisted Accuracy Score Evaluation Method for Machine-Centric Explainable Deep Neural Networks","display_name":"Heatmap Assisted Accuracy Score Evaluation Method for Machine-Centric Explainable Deep Neural Networks","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4285117207","doi":"https://doi.org/10.1109/access.2022.3184453"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3184453","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3184453","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09800759.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09800759.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085052759","display_name":"Junhee Lee","orcid":"https://orcid.org/0000-0003-4057-5848"},"institutions":[{"id":"https://openalex.org/I35928602","display_name":"Kyung Hee University","ror":"https://ror.org/01zqcg218","country_code":"KR","type":"education","lineage":["https://openalex.org/I35928602"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Junhee Lee","raw_affiliation_strings":["Department of Information Display, Kyung Hee University, Seoul, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0003-4057-5848","affiliations":[{"raw_affiliation_string":"Department of Information Display, Kyung Hee University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I35928602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082353286","display_name":"Hyeonseong Cho","orcid":null},"institutions":[{"id":"https://openalex.org/I35928602","display_name":"Kyung Hee University","ror":"https://ror.org/01zqcg218","country_code":"KR","type":"education","lineage":["https://openalex.org/I35928602"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyeonseong Cho","raw_affiliation_strings":["Department of Information Display, Kyung Hee University, Seoul, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Display, Kyung Hee University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I35928602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055635315","display_name":"Yun Jang Pyun","orcid":null},"institutions":[{"id":"https://openalex.org/I35928602","display_name":"Kyung Hee University","ror":"https://ror.org/01zqcg218","country_code":"KR","type":"education","lineage":["https://openalex.org/I35928602"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yun Jang Pyun","raw_affiliation_strings":["Department of Information Display, Kyung Hee University, Seoul, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Display, Kyung Hee University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I35928602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084904773","display_name":"Suk\u2010Ju Kang","orcid":"https://orcid.org/0000-0002-4809-956X"},"institutions":[{"id":"https://openalex.org/I148751991","display_name":"Sogang University","ror":"https://ror.org/056tn4839","country_code":"KR","type":"education","lineage":["https://openalex.org/I148751991"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Suk-Ju Kang","raw_affiliation_strings":["Department of Electronic Engineering, Sogang University, Seoul, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0002-4809-956X","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Sogang University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I148751991"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022170543","display_name":"Hyoungsik Nam","orcid":"https://orcid.org/0000-0002-1646-7954"},"institutions":[{"id":"https://openalex.org/I35928602","display_name":"Kyung Hee University","ror":"https://ror.org/01zqcg218","country_code":"KR","type":"education","lineage":["https://openalex.org/I35928602"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyoungsik Nam","raw_affiliation_strings":["Department of Information Display, Kyung Hee University, Seoul, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0002-1646-7954","affiliations":[{"raw_affiliation_string":"Department of Information Display, Kyung Hee University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I35928602"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.6648,"has_fulltext":true,"cited_by_count":13,"citation_normalized_percentile":{"value":0.86306611,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"10","issue":null,"first_page":"64832","last_page":"64849"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9994000196456909,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9994000196456909,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9951000213623047,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9851999878883362,"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/mnist-database","display_name":"MNIST database","score":0.8117299675941467},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7989675998687744},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6819781064987183},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.607812762260437},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5807598233222961},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5283551812171936},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5219830274581909},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4796774387359619},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.47854501008987427},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38879138231277466},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14050036668777466}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.8117299675941467},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7989675998687744},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6819781064987183},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.607812762260437},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5807598233222961},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5283551812171936},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5219830274581909},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4796774387359619},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.47854501008987427},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38879138231277466},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14050036668777466},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3184453","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3184453","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09800759.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:721563be856a462a9ee868d1e41ee122","is_oa":true,"landing_page_url":"https://doaj.org/article/721563be856a462a9ee868d1e41ee122","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 64832-64849 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3184453","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3184453","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09800759.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7599999904632568}],"awards":[{"id":"https://openalex.org/G328693229","display_name":null,"funder_award_id":"NRF-2019R1F1A1061114","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4285117207.pdf","grobid_xml":"https://content.openalex.org/works/W4285117207.grobid-xml"},"referenced_works_count":64,"referenced_works":["https://openalex.org/W107619411","https://openalex.org/W1787224781","https://openalex.org/W1821462560","https://openalex.org/W1849277567","https://openalex.org/W1977177161","https://openalex.org/W1984020445","https://openalex.org/W2007339694","https://openalex.org/W2064675550","https://openalex.org/W2108598243","https://openalex.org/W2112796928","https://openalex.org/W2117539524","https://openalex.org/W2125847307","https://openalex.org/W2195388612","https://openalex.org/W2240067561","https://openalex.org/W2282821441","https://openalex.org/W2295107390","https://openalex.org/W2606722458","https://openalex.org/W2765793020","https://openalex.org/W2765811365","https://openalex.org/W2767272762","https://openalex.org/W2769100446","https://openalex.org/W2789876780","https://openalex.org/W2804532080","https://openalex.org/W2804860796","https://openalex.org/W2885195348","https://openalex.org/W2891503716","https://openalex.org/W2907492528","https://openalex.org/W2910096450","https://openalex.org/W2924065506","https://openalex.org/W2947851192","https://openalex.org/W2962750014","https://openalex.org/W2962858109","https://openalex.org/W2963125461","https://openalex.org/W2966501701","https://openalex.org/W2970302604","https://openalex.org/W2971714613","https://openalex.org/W2981201742","https://openalex.org/W2988916019","https://openalex.org/W3011628670","https://openalex.org/W3013529009","https://openalex.org/W3019166713","https://openalex.org/W3022746105","https://openalex.org/W3039614774","https://openalex.org/W3045832151","https://openalex.org/W3082020764","https://openalex.org/W3091589898","https://openalex.org/W3096831136","https://openalex.org/W3112404983","https://openalex.org/W3117961507","https://openalex.org/W3118608800","https://openalex.org/W3158342813","https://openalex.org/W3202145349","https://openalex.org/W4293861706","https://openalex.org/W6637373629","https://openalex.org/W6677919164","https://openalex.org/W6677995690","https://openalex.org/W6685133223","https://openalex.org/W6730035438","https://openalex.org/W6739575509","https://openalex.org/W6740789180","https://openalex.org/W6745730051","https://openalex.org/W6769478285","https://openalex.org/W6779900187","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W4386603768","https://openalex.org/W2950475743","https://openalex.org/W2886711096","https://openalex.org/W2750384547","https://openalex.org/W4380078352","https://openalex.org/W3046591097","https://openalex.org/W4389249638","https://openalex.org/W2733410219","https://openalex.org/W2901671134","https://openalex.org/W3183100063"],"abstract_inverted_index":{"There":[0],"have":[1,30],"existed":[2],"many":[3],"studies":[4],"about":[5],"the":[6,13,16,26,34,56,75,78,94,102,130,141,145,157,164,168,183,198,279,308,333,336,344,361,369,383],"explainable":[7],"artificial":[8],"intelligence":[9],"(XAI)":[10],"that":[11,47,118,305,348],"explains":[12],"logic":[14],"behind":[15],"complex":[17],"deep":[18],"neural":[19],"network":[20,165,185],"called":[21],"a":[22,98,108,133,150,178,272],"black":[23],"box.":[24],"At":[25],"same":[27,280],"time,":[28],"researchers":[29],"tried":[31],"to":[32,62,124,129,166,368],"evaluate":[33],"explainability":[35],"performance":[36],"of":[37,58,86,97,132,180,182,202,218,239,262,289,292,301,316,325,329,335],"various":[38],"XAIs.":[39],"However,":[40],"most":[41],"previous":[42],"evaluation":[43,193,290,311],"methods":[44],"are":[45,60,83,90],"human-centric,":[46],"is,":[48],"subjective,":[49],"where":[50],"they":[51,89],"rely":[52],"on":[53,68,93],"how":[54],"much":[55,321],"results":[57,291,377],"explanation":[59,122,152],"similar":[61],"what":[63,71,126],"people&#x2019;s":[64],"decision":[65,76,131],"is":[66,175,195,307,340],"based":[67],"rather":[69],"than":[70,313,327],"features":[72],"actually":[73,127],"affect":[74],"in":[77,197,382],"model.":[79],"Their":[80],"XAI":[81,216,372],"selections":[82],"also":[84],"dependent":[85],"datasets.":[87],"Furthermore,":[88],"focusing":[91],"only":[92],"output":[95],"variation":[96],"target":[99],"class.":[100],"On":[101],"other":[103,283],"hand,":[104],"this":[105],"paper":[106],"proposes":[107],"robust":[109,310],"heatmap":[110,146,159,357],"assisted":[111,160],"accuracy":[112,169,362,380],"score":[113],"(HAAS)":[114],"scheme":[115,194,339],"over":[116,186,213],"datasets":[117,317],"helps":[119],"selecting":[120],"machine-centric":[121],"algorithms":[123,217,373],"show":[125],"leads":[128],"given":[134,151,184],"classification":[135,200],"network.":[136,370],"The":[137,171,191,287,371],"proposed":[138,192,337],"method":[139,312],"modifies":[140],"input":[142,276],"image":[143,199],"with":[144,355,374],"scores":[147,358],"obtained":[148],"by":[149,299,365],"algorithm":[153],"and":[154,188,206,211,222,232,241,246,251,253,257,267,278,294,359],"then":[155],"puts":[156],"resultant":[158,172],"(HA)":[161],"images":[162,352],"into":[163],"estimate":[167],"change.":[170],"metric":[173],"(<i>HAAS</i>)":[174],"computed":[176],"as":[177,285],"ratio":[179],"accuracies":[181],"HA":[187,346,351,385],"original":[189],"images.":[190],"verified":[196],"models":[201],"LeNet-5":[203],"for":[204,208,230,264,274,282],"MNIST":[205],"VGG-16":[207],"CIFAR-10,":[209],"STL-10,":[210],"ILSVRC2012":[212,254],"totally":[214],"11":[215],"saliency":[219],"map,":[220],"deconvolution,":[221],"9":[223],"layer-wise":[224],"relevance":[225],"propagation":[226],"(LRP)":[227],"configurations.":[228],"Consequently,":[229],"LRP1":[231,260],"LRP3,":[233],"MINST":[234],"showed":[235],"largest":[236,375],"<i>HAAS</i>":[237,376],"values":[238],"1.0088":[240],"1.0079,":[242],"CIFAR-10":[243],"achieved":[244],"1.1160":[245],"1.1254,":[247],"STL-10":[248],"had":[249],"1.0906":[250],"1.0918,":[252],"got":[255],"1.3207":[256],"1.3469.":[258],"While":[259],"consists":[261],"&#x03F5;-rules":[263,281],"input,":[265],"convolutional,":[266],"fully-connected":[268],"layers,":[269],"LRP3":[270],"adopts":[271],"bounded-rule":[273],"an":[275],"layer":[277],"layers":[284],"LRP1.":[286],"consistency":[288],"HAAS":[293,306,319,338],"AOPC":[295,314,328],"has":[296,320],"been":[297],"compared":[298],"means":[300],"Kullback-Leibler":[302],"divergence,":[303],"ensuring":[304],"more":[309],"independently":[315],"since":[318],"lower":[322],"average":[323],"divergence":[324],"0.0251":[326],"0.3048.":[330],"In":[331],"addition,":[332],"validity":[334],"further":[341],"investigated":[342],"through":[343],"inverted":[345,350,356,384],"test":[347],"employs":[349],"made":[353],"up":[354],"estimates":[360],"degradation":[363,381],"caused":[364],"applying":[366],"them":[367],"experience":[378],"biggest":[379],"test.":[386]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}