{"id":"https://openalex.org/W3011037064","doi":"https://doi.org/10.1117/12.2549298","title":"Explainable AI for medical imaging: deep-learning CNN ensemble for classification of estrogen receptor status from breast MRI","display_name":"Explainable AI for medical imaging: deep-learning CNN ensemble for classification of estrogen receptor status from breast MRI","publication_year":2020,"publication_date":"2020-03-16","ids":{"openalex":"https://openalex.org/W3011037064","doi":"https://doi.org/10.1117/12.2549298","mag":"3011037064"},"language":"en","primary_location":{"id":"doi:10.1117/12.2549298","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2549298","pdf_url":null,"source":{"id":"https://openalex.org/S4306519512","display_name":"Medical Imaging 2020: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"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":"Medical Imaging 2020: Computer-Aided Diagnosis","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/A5043382141","display_name":"Zachary Papanastasopoulos","orcid":null},"institutions":[{"id":"https://openalex.org/I4210111179","display_name":"Michigan United","ror":"https://ror.org/0291ys696","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210111179"]},{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zachary Papanastasopoulos","raw_affiliation_strings":["Univ. of Michigan (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of Michigan (United States)","institution_ids":["https://openalex.org/I4210111179","https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019785665","display_name":"Ravi K. Samala","orcid":"https://orcid.org/0000-0002-6661-4801"},"institutions":[{"id":"https://openalex.org/I4210111179","display_name":"Michigan United","ror":"https://ror.org/0291ys696","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210111179"]},{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ravi K. Samala","raw_affiliation_strings":["Univ. of Michigan (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of Michigan (United States)","institution_ids":["https://openalex.org/I4210111179","https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027247097","display_name":"Heang\u2010Ping Chan","orcid":"https://orcid.org/0000-0001-7777-9006"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heang-Ping Chan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087281080","display_name":"Lubomir M. Hadjiiski","orcid":"https://orcid.org/0000-0003-2069-8066"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]},{"id":"https://openalex.org/I4210111179","display_name":"Michigan United","ror":"https://ror.org/0291ys696","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210111179"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lubomir Hadjiiski","raw_affiliation_strings":["Univ. of Michigan (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of Michigan (United States)","institution_ids":["https://openalex.org/I4210111179","https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045832765","display_name":"Chintana Paramagul","orcid":null},"institutions":[{"id":"https://openalex.org/I4210111179","display_name":"Michigan United","ror":"https://ror.org/0291ys696","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210111179"]},{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chintana Paramagul","raw_affiliation_strings":["Univ. of Michigan (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of Michigan (United States)","institution_ids":["https://openalex.org/I4210111179","https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073769753","display_name":"Mark A. Helvie","orcid":"https://orcid.org/0000-0002-4415-4525"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]},{"id":"https://openalex.org/I4210111179","display_name":"Michigan United","ror":"https://ror.org/0291ys696","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210111179"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mark A. Helvie","raw_affiliation_strings":["Univ. of Michigan (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of Michigan (United States)","institution_ids":["https://openalex.org/I4210111179","https://openalex.org/I27837315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003795059","display_name":"Colleen H. Neal","orcid":"https://orcid.org/0000-0002-4041-8394"},"institutions":[{"id":"https://openalex.org/I4210111179","display_name":"Michigan United","ror":"https://ror.org/0291ys696","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210111179"]},{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Colleen H. Neal","raw_affiliation_strings":["Univ. of Michigan (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of Michigan (United States)","institution_ids":["https://openalex.org/I4210111179","https://openalex.org/I27837315"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5043382141"],"corresponding_institution_ids":["https://openalex.org/I27837315","https://openalex.org/I4210111179"],"apc_list":null,"apc_paid":null,"fwci":6.7997,"has_fulltext":false,"cited_by_count":73,"citation_normalized_percentile":{"value":0.97428634,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"52","last_page":"52"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9914000034332275,"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":0.9914000034332275,"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":0.9883999824523926,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.9111999869346619,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6326802968978882},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5732475519180298},{"id":"https://openalex.org/keywords/estrogen-receptor","display_name":"Estrogen receptor","score":0.5397923588752747},{"id":"https://openalex.org/keywords/breast-imaging","display_name":"Breast imaging","score":0.5136393904685974},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.496359646320343},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.46508166193962097},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.44906747341156006},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4123958945274353},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3871215581893921},{"id":"https://openalex.org/keywords/mammography","display_name":"Mammography","score":0.29751041531562805},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2789136469364166},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.21869447827339172},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.18009811639785767}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6326802968978882},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5732475519180298},{"id":"https://openalex.org/C84606932","wikidata":"https://www.wikidata.org/wiki/Q416496","display_name":"Estrogen receptor","level":4,"score":0.5397923588752747},{"id":"https://openalex.org/C2777432617","wikidata":"https://www.wikidata.org/wiki/Q22905905","display_name":"Breast imaging","level":5,"score":0.5136393904685974},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.496359646320343},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.46508166193962097},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.44906747341156006},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4123958945274353},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3871215581893921},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.29751041531562805},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2789136469364166},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.21869447827339172},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.18009811639785767},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2549298","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2549298","pdf_url":null,"source":{"id":"https://openalex.org/S4306519512","display_name":"Medical Imaging 2020: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"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":"Medical Imaging 2020: Computer-Aided Diagnosis","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":0,"referenced_works":[],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W2731899572","https://openalex.org/W4230611425","https://openalex.org/W3124943098","https://openalex.org/W4308112567","https://openalex.org/W3162132941","https://openalex.org/W3024479225","https://openalex.org/W3171371563","https://openalex.org/W3003847115"],"abstract_inverted_index":{"Deep-learning":[0],"convolutional":[1],"neural":[2],"networks":[3],"(DCNNs)":[4],"are":[5],"the":[6,30,63,87,126,135,138,143,159,162,173,181,187,193,196,208,218,222],"most":[7],"commonly":[8],"used":[9,23],"approach":[10],"in":[11,217],"medical":[12],"image":[13],"analysis":[14],"tasks":[15],"at":[16],"present;":[17],"however,":[18],"they":[19],"have":[20],"largely":[21],"been":[22],"as":[24,151],"black-box":[25],"predictors,":[26],"lacking":[27],"explanation":[28],"for":[29],"underlying":[31],"reasons.":[32],"Explainable":[33],"artificial":[34],"intelligence":[35],"(XAI)":[36],"is":[37],"an":[38,60],"emerging":[39],"subfield":[40],"of":[41,86,112,147],"AI":[42],"seeking":[43],"to":[44,58,70,192,242],"understand":[45],"how":[46],"models":[47],"make":[48],"their":[49],"decisions.":[50],"In":[51],"this":[52],"work,":[53],"we":[54,176],"applied":[55,177],"XAI":[56,178],"visualization":[57],"gain":[59],"insight":[61],"into":[62],"features":[64,206,220,232],"learned":[65,204],"by":[66],"a":[67,106,110,118],"DCNN":[68,122,174,203,228],"trained":[69,129],"classify":[71],"estrogen":[72],"receptor":[73],"status":[74],"(ER+":[75],"vs":[76],"ER-)":[77],"based":[78],"on":[79,130],"dynamic":[80,141,211],"contrast-enhanced":[81],"magnetic":[82],"resonance":[83],"imaging":[84],"(DCEMRI)":[85],"breast.":[88],"Our":[89],"data":[90,132,245],"set":[91],"contained":[92],"1395":[93],"ER+":[94],"regions-of-interest":[95],"(ROIs)":[96],"and":[97,109,140,186,210,246],"729":[98],"ER-":[99],"ROIs":[100,194],"from":[101,125,168,195,207,221,230,234],"148":[102],"patients,":[103],"each":[104,148],"with":[105,158],"pre-contrast":[107],"scan":[108],"minimum":[111],"two":[113,223],"post-contrast":[114],"scans.":[115],"We":[116,199,225],"developed":[117],"novel":[119],"transfer-trained":[120],"dual-domain":[121],"architecture":[123],"derived":[124],"AlexNet":[127],"model":[128],"ImageNet":[131],"that":[133,201],"received":[134],"spatial":[136,209],"(across":[137,142],"volume)":[139],"acquisition":[144],"sequence)":[145],"components":[146],"DCE-MRI":[149],"ROI":[150],"input.":[152],"The":[153],"network\u2019s":[154],"performance":[155],"was":[156],"evaluated":[157],"area":[160],"under":[161],"receiver":[163],"operating":[164],"characteristic":[165],"curve":[166],"(AUC)":[167],"leave-one-case-out":[169],"crossvalidation.":[170],"To":[171],"visualize":[172],"learning,":[175],"techniques,":[179],"including":[180],"Integrated":[182],"Gradients":[183],"attribution":[184],"method":[185],"SmoothGrad":[188],"noise":[189],"reduction":[190],"algorithm,":[191],"training":[197,247],"set.":[198],"observed":[200],"our":[202,244,248],"relevant":[205],"domains,":[212],"but":[213],"there":[214],"were":[215],"differences":[216],"contributing":[219],"domains.":[224],"also":[226],"visualized":[227],"learning":[229],"irrelevant":[231],"resulting":[233],"pre-processing":[235,243],"artifacts.":[236],"These":[237],"observations":[238],"motivate":[239],"new":[240],"approaches":[241],"DCNN.":[249]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":22},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":5}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-10-10T00:00:00"}
