{"id":"https://openalex.org/W4416183534","doi":"https://doi.org/10.1109/sibgrapi67909.2025.11223416","title":"A Deep Learning Framework for Pulmonary Disease Classification Using Volume-Rendered CTs","display_name":"A Deep Learning Framework for Pulmonary Disease Classification Using Volume-Rendered CTs","publication_year":2025,"publication_date":"2025-09-30","ids":{"openalex":"https://openalex.org/W4416183534","doi":"https://doi.org/10.1109/sibgrapi67909.2025.11223416"},"language":null,"primary_location":{"id":"doi:10.1109/sibgrapi67909.2025.11223416","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sibgrapi67909.2025.11223416","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 38th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5019543231","display_name":"Noemi Maritza Lapa Romero","orcid":null},"institutions":[{"id":"https://openalex.org/I4210147991","display_name":"Instituto de Informaci\u00f3n Cient\u00ed\ufb01ca y Tecnol\u00f3gica","ror":"https://ror.org/05m8k7t58","country_code":"CU","type":"facility","lineage":["https://openalex.org/I4210147991"]}],"countries":["CU"],"is_corresponding":false,"raw_author_name":"Noemi Maritza L. Romero","raw_affiliation_strings":["Instituto de Inform&#x00E1;tica, UFRGS"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Instituto de Inform&#x00E1;tica, UFRGS","institution_ids":["https://openalex.org/I4210147991"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ricco V. C. Soares","orcid":null},"institutions":[{"id":"https://openalex.org/I4210147991","display_name":"Instituto de Informaci\u00f3n Cient\u00ed\ufb01ca y Tecnol\u00f3gica","ror":"https://ror.org/05m8k7t58","country_code":"CU","type":"facility","lineage":["https://openalex.org/I4210147991"]}],"countries":["CU"],"is_corresponding":false,"raw_author_name":"Ricco V. C. Soares","raw_affiliation_strings":["Instituto de Inform&#x00E1;tica, UFRGS"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Instituto de Inform&#x00E1;tica, UFRGS","institution_ids":["https://openalex.org/I4210147991"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039060913","display_name":"Mariana Recamonde\u2010Mendoza","orcid":"https://orcid.org/0000-0003-2800-1032"},"institutions":[{"id":"https://openalex.org/I4210147991","display_name":"Instituto de Informaci\u00f3n Cient\u00ed\ufb01ca y Tecnol\u00f3gica","ror":"https://ror.org/05m8k7t58","country_code":"CU","type":"facility","lineage":["https://openalex.org/I4210147991"]}],"countries":["CU"],"is_corresponding":false,"raw_author_name":"Mariana Recamonde-Mendoza","raw_affiliation_strings":["Instituto de Inform&#x00E1;tica, UFRGS"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Instituto de Inform&#x00E1;tica, UFRGS","institution_ids":["https://openalex.org/I4210147991"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029235009","display_name":"Jo\u00e3o L. D. Comba","orcid":"https://orcid.org/0000-0003-2921-2130"},"institutions":[{"id":"https://openalex.org/I4210147991","display_name":"Instituto de Informaci\u00f3n Cient\u00ed\ufb01ca y Tecnol\u00f3gica","ror":"https://ror.org/05m8k7t58","country_code":"CU","type":"facility","lineage":["https://openalex.org/I4210147991"]}],"countries":["CU"],"is_corresponding":false,"raw_author_name":"Jo\u00e3o L. D. Comba","raw_affiliation_strings":["Instituto de Inform&#x00E1;tica, UFRGS"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Instituto de Inform&#x00E1;tica, UFRGS","institution_ids":["https://openalex.org/I4210147991"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210147991"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.54654994,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.8216000199317932,"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":0.8216000199317932,"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/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.09390000253915787,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.00860000029206276,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/deep-learning","display_name":"Deep learning","score":0.7767000198364258},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.489300012588501},{"id":"https://openalex.org/keywords/pulmonary-disease","display_name":"Pulmonary disease","score":0.4277999997138977},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3808000087738037},{"id":"https://openalex.org/keywords/lung-disease","display_name":"Lung disease","score":0.349700003862381},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3472999930381775}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7767000198364258},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7300000190734863},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5400000214576721},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5365999937057495},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.489300012588501},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.4749999940395355},{"id":"https://openalex.org/C2992779976","wikidata":"https://www.wikidata.org/wiki/Q3286546","display_name":"Pulmonary disease","level":2,"score":0.4277999997138977},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3808000087738037},{"id":"https://openalex.org/C2983914783","wikidata":"https://www.wikidata.org/wiki/Q3286546","display_name":"Lung disease","level":3,"score":0.349700003862381},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3472999930381775},{"id":"https://openalex.org/C544519230","wikidata":"https://www.wikidata.org/wiki/Q32566","display_name":"Computed tomography","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2985000014305115},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.2759999930858612},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.26019999384880066}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sibgrapi67909.2025.11223416","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sibgrapi67909.2025.11223416","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 38th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1993227690","https://openalex.org/W2109845109","https://openalex.org/W2624767455","https://openalex.org/W2962858109","https://openalex.org/W2969305626","https://openalex.org/W3009200419","https://openalex.org/W3010659930","https://openalex.org/W3011149445","https://openalex.org/W3028070348","https://openalex.org/W3054666633","https://openalex.org/W3092624683","https://openalex.org/W3138985726","https://openalex.org/W3157726815","https://openalex.org/W3159019058","https://openalex.org/W3193918407","https://openalex.org/W3202353378","https://openalex.org/W4220928895","https://openalex.org/W4319294815","https://openalex.org/W4387770501"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"classification":[1,56],"of":[2],"pulmonary":[3,82],"diseases":[4],"is":[5],"critical":[6],"for":[7,132],"clinical":[8],"decision-making,":[9],"and":[10,38,79,103,116],"deep":[11],"learning":[12],"models":[13],"using":[14,88],"chest":[15],"CT":[16,32],"scans":[17],"have":[18],"become":[19],"a":[20,54,92,104,128],"key":[21],"tool":[22],"in":[23],"this":[24],"task.":[25],"Most":[26],"existing":[27],"approaches":[28],"rely":[29],"on":[30],"2D":[31],"slices,":[33],"which":[34,96],"provide":[35],"limited":[36],"views":[37],"may":[39],"miss":[40],"important":[41],"spatial":[42],"patterns":[43],"across":[44],"the":[45,74,86],"lung":[46,133],"volume.":[47],"To":[48],"address":[49],"this,":[50],"we":[51],"introduce":[52],"CT-VR,":[53],"novel":[55],"approach":[57,112],"that":[58,110],"leverages":[59],"3D":[60],"volume-rendered":[61],"images":[62],"captured":[63],"from":[64,100],"multiple":[65],"angles.":[66],"By":[67],"incorporating":[68],"multi-view":[69],"volume":[70],"rendering,":[71],"CT-VR":[72],"enhances":[73],"model":[75],"ability":[76],"to":[77,120],"detect":[78],"differentiate":[80],"between":[81],"conditions.":[83],"We":[84],"evaluate":[85],"method":[87],"COVID-19":[89],"datasets":[90,99],"as":[91,127],"primary":[93],"case":[94],"study,":[95],"include":[97],"private":[98],"partner":[101],"hospitals":[102],"publicly":[105],"available":[106],"benchmark.":[107],"Results":[108],"demonstrate":[109],"our":[111],"improves":[113],"lesion":[114],"identification":[115],"delivers":[117],"performance":[118],"compared":[119],"traditional":[121],"slice-based":[122],"models,":[123],"highlighting":[124],"its":[125],"potential":[126],"more":[129],"effective":[130],"solution":[131],"disease":[134],"classification.":[135]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-11-11T00:00:00"}
