{"id":"https://openalex.org/W7168249378","doi":"https://doi.org/10.19153/cleiej.29.3.6","title":"Explainable COVID-19 Classification Via Variational and Perceptual Autoencoder-Guided Occlusion","display_name":"Explainable COVID-19 Classification Via Variational and Perceptual Autoencoder-Guided Occlusion","publication_year":2026,"publication_date":"2026-07-14","ids":{"openalex":"https://openalex.org/W7168249378","doi":"https://doi.org/10.19153/cleiej.29.3.6"},"language":"en","primary_location":{"id":"doi:10.19153/cleiej.29.3.6","is_oa":true,"landing_page_url":"https://doi.org/10.19153/cleiej.29.3.6","pdf_url":"https://clei.org/cleiej/index.php/cleiej/article/download/1032/584","source":{"id":"https://openalex.org/S164249298","display_name":"CLEI electronic journal","issn_l":"0717-5000","issn":["0717-5000"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310315484","host_organization_name":"Latin American Center for Computer Studies","host_organization_lineage":["https://openalex.org/P4310315484"],"host_organization_lineage_names":["Latin American Center for Computer Studies"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"CLEI Electronic Journal","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://clei.org/cleiej/index.php/cleiej/article/download/1032/584","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140650450","display_name":"Rodrigo Bayuk","orcid":null},"institutions":[{"id":"https://openalex.org/I4210106007","display_name":"Universidad Andr\u00e9s Bello","ror":null,"country_code":"CL","type":null,"lineage":["https://openalex.org/I4210106007"]}],"countries":["CL"],"is_corresponding":false,"raw_author_name":"Rodrigo Bayuk","raw_affiliation_strings":["Universidad Andres Bello , Facultad de Ingenier\u00eda , Santiago , Chile , 11300 ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad Andres Bello , Facultad de Ingenier\u00eda , Santiago , Chile , 11300 ,","institution_ids":["https://openalex.org/I4210106007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140652017","display_name":"Joel Manquel","orcid":null},"institutions":[{"id":"https://openalex.org/I4210106007","display_name":"Universidad Andr\u00e9s Bello","ror":null,"country_code":"CL","type":null,"lineage":["https://openalex.org/I4210106007"]}],"countries":["CL"],"is_corresponding":false,"raw_author_name":"Joel Manquel","raw_affiliation_strings":["Universidad Andres Bello , Facultad de Ingenier\u00eda , Santiago , Chile , 11300 ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad Andres Bello , Facultad de Ingenier\u00eda , Santiago , Chile , 11300 ,","institution_ids":["https://openalex.org/I4210106007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140679235","display_name":"Orietta Nicolis","orcid":null},"institutions":[{"id":"https://openalex.org/I4210106007","display_name":"Universidad Andr\u00e9s Bello","ror":null,"country_code":"CL","type":null,"lineage":["https://openalex.org/I4210106007"]}],"countries":["CL"],"is_corresponding":false,"raw_author_name":"Orietta Nicolis","raw_affiliation_strings":["Universidad Andres Bello , Facultad de Ingenier\u00eda , Santiago , Chile , 11300 ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad Andres Bello , Facultad de Ingenier\u00eda , Santiago , Chile , 11300 ,","institution_ids":["https://openalex.org/I4210106007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140652933","display_name":"Billy Peralta","orcid":null},"institutions":[{"id":"https://openalex.org/I13897259","display_name":"Universidad Andr\u00e9s Bello","ror":"https://ror.org/01qq57711","country_code":"CL","type":"education","lineage":["https://openalex.org/I13897259"]}],"countries":["CL"],"is_corresponding":false,"raw_author_name":"Billy Peralta","raw_affiliation_strings":["Universidad Andres Bello"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad Andres Bello","institution_ids":["https://openalex.org/I13897259"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5140585333","display_name":"Luis Caro","orcid":null},"institutions":[{"id":"https://openalex.org/I2802558902","display_name":"Universidad Cat\u00f3lica de Temuco","ror":"https://ror.org/051nvp675","country_code":"CL","type":"education","lineage":["https://openalex.org/I2802558902"]}],"countries":["CL"],"is_corresponding":false,"raw_author_name":"Luis Caro","raw_affiliation_strings":["Universidad Cat\u00f3lica de Temuco , Departamento de Ingenier\u00eda Civil en Inform\u00e1tica , Temuco , Chile , 11300 ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad Cat\u00f3lica de Temuco , Departamento de Ingenier\u00eda Civil en Inform\u00e1tica , Temuco , Chile , 11300 ,","institution_ids":["https://openalex.org/I2802558902"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.89861153,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"29","issue":"3","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9702000021934509,"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"}},"topics":[{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9702000021934509,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.0032999999821186066,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.002400000113993883,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8762000203132629},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5631999969482422},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5151000022888184},{"id":"https://openalex.org/keywords/masking","display_name":"Masking (illustration)","score":0.4984999895095825},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.4634999930858612},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.46070000529289246},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4251999855041504}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8762000203132629},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6697999835014343},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6571000218391418},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5631999969482422},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5151000022888184},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.4984999895095825},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.4634999930858612},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.46070000529289246},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43220001459121704},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4251999855041504},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35350000858306885},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3474000096321106},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3292999863624573},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.3098999857902527},{"id":"https://openalex.org/C22033958","wikidata":"https://www.wikidata.org/wiki/Q7167036","display_name":"Perceptual learning","level":3,"score":0.3012000024318695},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C65185188","wikidata":"https://www.wikidata.org/wiki/Q107775","display_name":"Streak","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C3020695711","wikidata":"https://www.wikidata.org/wiki/Q3631290","display_name":"Lung infection","level":3,"score":0.25859999656677246}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.19153/cleiej.29.3.6","is_oa":true,"landing_page_url":"https://doi.org/10.19153/cleiej.29.3.6","pdf_url":"https://clei.org/cleiej/index.php/cleiej/article/download/1032/584","source":{"id":"https://openalex.org/S164249298","display_name":"CLEI electronic journal","issn_l":"0717-5000","issn":["0717-5000"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310315484","host_organization_name":"Latin American Center for Computer Studies","host_organization_lineage":["https://openalex.org/P4310315484"],"host_organization_lineage_names":["Latin American Center for Computer Studies"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"CLEI Electronic Journal","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:5c46757cd7314b8baf4a13a90caeb676","is_oa":false,"landing_page_url":"https://doaj.org/article/5c46757cd7314b8baf4a13a90caeb676","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"CLEI Electronic Journal, Vol 29, Iss 3 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.19153/cleiej.29.3.6","is_oa":true,"landing_page_url":"https://doi.org/10.19153/cleiej.29.3.6","pdf_url":"https://clei.org/cleiej/index.php/cleiej/article/download/1032/584","source":{"id":"https://openalex.org/S164249298","display_name":"CLEI electronic journal","issn_l":"0717-5000","issn":["0717-5000"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310315484","host_organization_name":"Latin American Center for Computer Studies","host_organization_lineage":["https://openalex.org/P4310315484"],"host_organization_lineage_names":["Latin American Center for Computer Studies"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"CLEI Electronic Journal","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7102265357971191,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[{"id":"https://openalex.org/G3659963728","display_name":"MULTIMODAL SPATIO-TEMPORAL INTELLIGENT TRANSPORTATION SYSTEM USING ADAPTIVE MACHINE LEARNING","funder_award_id":"1241882","funder_id":"https://openalex.org/F4320331146","funder_display_name":"Agencia Nacional de Investigaci\u00f3n y Desarrollo"},{"id":"https://openalex.org/G7877382544","display_name":"NATIONAL CENTER FOR ARTIFICIAL INTELLIGENCE RESEARCH (NAIR)","funder_award_id":"FB210017","funder_id":"https://openalex.org/F4320331146","funder_display_name":"Agencia Nacional de Investigaci\u00f3n y Desarrollo"}],"funders":[{"id":"https://openalex.org/F4320319253","display_name":"Agen\u021bia Na\u021bional\u0103 pentru Cercetare \u0219i Dezvoltare","ror":null},{"id":"https://openalex.org/F4320331146","display_name":"Agencia Nacional de Investigaci\u00f3n y Desarrollo","ror":null},{"id":"https://openalex.org/F4320338463","display_name":"CHIST-ERA","ror":"https://ror.org/00rbzpz17"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7168249378.pdf","grobid_xml":"https://content.openalex.org/works/W7168249378.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Technology":[0],"played":[1],"a":[2,53,120,135,142],"crucial":[3],"role":[4],"in":[5,11,27,44,62,92,105],"combating":[6],"the":[7,12,18,22,28,35,45,57,87,189,196,201,206,210,213,229],"COVID":[8],"pandemic,":[9],"both":[10,166],"rapid":[13],"development":[14],"of":[15,21,37,56,195,212,234],"vaccines":[16],"and":[17,59,85,131,150,171,232],"early":[19,235],"detection":[20,237],"virus.":[23],"Consequently,":[24],"numerous":[25],"studies":[26],"medical":[29,46],"field":[30],"have":[31,52,72],"focused":[32],"on":[33,124],"leveraging":[34],"power":[36],"artificial":[38,78],"intelligence":[39,79],"for":[40],"COVID-19":[41,104,236],"detection.":[42,94],"However,":[43],"domain,":[47],"it":[48],"is":[49],"essential":[50],"to":[51,75,83,102,127,145,160,182,227],"clear":[54],"understanding":[55],"processes":[58],"algorithms":[60,90],"used":[61,91,159],"decision-making,":[63],"as":[64],"these":[65],"directly":[66],"impact":[67],"people\u2019s":[68],"health.":[69],"Therefore,":[70],"efforts":[71],"been":[73],"made":[74],"implement":[76],"explainable":[77],"techniques,":[80],"enabling":[81,179],"humans":[82],"understand":[84],"explain":[86],"deep":[88,218],"learning":[89,219],"disease":[93],"In":[95],"this":[96,223],"work,":[97],"we":[98,118,132,225],"present":[99],"an":[100],"approach":[101],"detecting":[103],"chest":[106,239],"X-rays":[107],"that":[108,140],"combines":[109],"reconstruction-based":[110],"anomaly":[111],"discov-":[112],"ery":[113],"with":[114],"perturbation-based":[115],"attribution.":[116],"Specifically,":[117],"use":[119],"Variational":[121],"Autoencoder":[122,138],"trained":[123],"healthy":[125],"lungs":[126],"identify":[128],"lung":[129],"anomalies,":[130],"additionally":[133],"evaluate":[134],"Per-":[136],"ceptual":[137],"(PAE)":[139],"incorporates":[141],"perceptual":[143],"loss":[144],"produce":[146],"sharper":[147],"recon-":[148],"structions":[149],"more":[151,186],"contrastive":[152],"residual":[153],"maps.":[154],"These":[155],"signals":[156],"are":[157],"then":[158],"highlight":[161],"critical":[162],"image":[163],"evidence":[164],"through":[165,238],"patch-level":[167],"scoring":[168],"(grid-based":[169],"occlusion)":[170],"pixel-":[172],"level":[173],"masking":[174],"derived":[175],"from":[176],"reconstruction-error":[177],"maps,":[178],"healthcare":[180],"professionals":[181],"localize":[183],"relevant":[184],"regions":[185],"effectively.":[187],"Moreover,":[188],"proposed":[190],"framework":[191],"provides":[192],"clearer":[193],"explanations":[194],"model\u2019s":[197],"decisions":[198],"by":[199,217],"quantifying":[200],"prediction":[202],"change":[203],"after":[204],"occluding":[205],"identified":[207],"regions,":[208],"reducing":[209],"complexity":[211],"\u201cblack":[214],"boxes\u201d":[215],"generated":[216],"neural":[220],"networks.":[221],"With":[222],"methodology,":[224],"aim":[226],"improve":[228],"effec-":[230],"tiveness":[231],"reliability":[233],"X-rays.":[240]},"counts_by_year":[],"updated_date":"2026-07-16T05:54:29.318815","created_date":"2026-07-15T00:00:00"}