{"id":"https://openalex.org/W4408354418","doi":"https://doi.org/10.1109/icassp49660.2025.10890468","title":"SOLVE: Spatially Optimized Lung Volume Evidence Model for Efficient Nodule Malignancy Classification","display_name":"SOLVE: Spatially Optimized Lung Volume Evidence Model for Efficient Nodule Malignancy Classification","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408354418","doi":"https://doi.org/10.1109/icassp49660.2025.10890468"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10890468","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10890468","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5071473768","display_name":"Sadaf Khademi","orcid":null},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Sadaf Khademi","raw_affiliation_strings":["Concordia University,Concordia Inst. for Info. Syst. Eng.,Montreal,Canada"],"affiliations":[{"raw_affiliation_string":"Concordia University,Concordia Inst. for Info. Syst. Eng.,Montreal,Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027263568","display_name":"Anastasia Oikonomou","orcid":"https://orcid.org/0000-0001-6996-237X"},"institutions":[{"id":"https://openalex.org/I2802362269","display_name":"Health Sciences Centre","ror":"https://ror.org/05pr37258","country_code":"CA","type":"funder","lineage":["https://openalex.org/I110535807","https://openalex.org/I2802362269"]},{"id":"https://openalex.org/I1323843004","display_name":"Sunnybrook Health Science Centre","ror":"https://ror.org/03wefcv03","country_code":"CA","type":"healthcare","lineage":["https://openalex.org/I1323843004"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Anastasia Oikonomou","raw_affiliation_strings":["Sunnybrook Health Sciences Centre,Dept. of Medical Imaging,Toronto,Canada"],"affiliations":[{"raw_affiliation_string":"Sunnybrook Health Sciences Centre,Dept. of Medical Imaging,Toronto,Canada","institution_ids":["https://openalex.org/I1323843004","https://openalex.org/I2802362269"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058253407","display_name":"Arash Mohammadi","orcid":"https://orcid.org/0000-0003-1972-7923"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Arash Mohammadi","raw_affiliation_strings":["Concordia University,Concordia Inst. for Info. Syst. Eng.,Montreal,Canada"],"affiliations":[{"raw_affiliation_string":"Concordia University,Concordia Inst. for Info. Syst. Eng.,Montreal,Canada","institution_ids":["https://openalex.org/I60158472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5071473768"],"corresponding_institution_ids":["https://openalex.org/I60158472"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07718523,"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":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9966999888420105,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9966999888420105,"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":0.9902999997138977,"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/volume","display_name":"Volume (thermodynamics)","score":0.6511107683181763},{"id":"https://openalex.org/keywords/nodule","display_name":"Nodule (geology)","score":0.6386737823486328},{"id":"https://openalex.org/keywords/malignancy","display_name":"Malignancy","score":0.6120288968086243},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5671597123146057},{"id":"https://openalex.org/keywords/lung","display_name":"Lung","score":0.4840223491191864},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.35534486174583435},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3488498330116272},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.27583515644073486},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.2422163188457489},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.12670692801475525},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.11761456727981567},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09438309073448181}],"concepts":[{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.6511107683181763},{"id":"https://openalex.org/C2776731575","wikidata":"https://www.wikidata.org/wiki/Q2916245","display_name":"Nodule (geology)","level":2,"score":0.6386737823486328},{"id":"https://openalex.org/C2779399171","wikidata":"https://www.wikidata.org/wiki/Q1483951","display_name":"Malignancy","level":2,"score":0.6120288968086243},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5671597123146057},{"id":"https://openalex.org/C2777714996","wikidata":"https://www.wikidata.org/wiki/Q7886","display_name":"Lung","level":2,"score":0.4840223491191864},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.35534486174583435},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3488498330116272},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.27583515644073486},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.2422163188457489},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.12670692801475525},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.11761456727981567},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09438309073448181},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10890468","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10890468","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":28,"referenced_works":["https://openalex.org/W1989734503","https://openalex.org/W2039400714","https://openalex.org/W2043630083","https://openalex.org/W2045968318","https://openalex.org/W2066931620","https://openalex.org/W2085711142","https://openalex.org/W2108598243","https://openalex.org/W2147023528","https://openalex.org/W2162950292","https://openalex.org/W2254976097","https://openalex.org/W2613848278","https://openalex.org/W2732010380","https://openalex.org/W2939205828","https://openalex.org/W3015650181","https://openalex.org/W3024083990","https://openalex.org/W3132705981","https://openalex.org/W3136207749","https://openalex.org/W3136605096","https://openalex.org/W3138516171","https://openalex.org/W4235944801","https://openalex.org/W4307955209","https://openalex.org/W4310672790","https://openalex.org/W4366668465","https://openalex.org/W4367692227","https://openalex.org/W4377010708","https://openalex.org/W4385245566","https://openalex.org/W4399801880","https://openalex.org/W4402915792"],"related_works":["https://openalex.org/W2092561569","https://openalex.org/W2377100155","https://openalex.org/W1964806738","https://openalex.org/W2793887421","https://openalex.org/W4389671191","https://openalex.org/W3153228984","https://openalex.org/W4243779904","https://openalex.org/W2798121181","https://openalex.org/W2965938661","https://openalex.org/W2428333999"],"abstract_inverted_index":{"Lung":[0,26],"cancer":[1],"diagnosis":[2],"remains":[3],"a":[4,33,82,107,115,167],"critical":[5],"challenge":[6,137],"in":[7,56,170],"personalized":[8],"medicine,":[9],"demanding":[10],"novel":[11,34],"approaches":[12],"for":[13,114,126],"efficient":[14],"and":[15,47,86,128],"accurate":[16],"prediction.":[17],"In":[18],"this":[19],"context,":[20],"we":[21],"propose":[22],"the":[23,70,121,130,136,149],"Spatially":[24],"Optimized":[25],"Volume":[27],"Evidence":[28],"(SOLVE)":[29],"framework,":[30],"which":[31],"is":[32],"lung":[35],"malignancy":[36],"prediction":[37,171],"model":[38],"developed":[39],"by":[40],"integrating":[41,62],"principles":[42],"from":[43,90],"brain-inspired":[44],"evidence":[45,63],"accumulation":[46,64],"retina-inspired":[48],"data":[49],"processing.":[50],"SOLVE":[51,80,143,165],"introduces":[52],"spatial":[53,78],"scale":[54],"optimization":[55],"Computed":[57],"Tomography":[58],"(CT)":[59],"scan":[60],"analysis,":[61],"concepts":[65],"to":[66,73,92],"enhance":[67],"decision-making.":[68],"Mimicking":[69],"retina\u2019s":[71],"ability":[72],"process":[74],"images":[75],"across":[76],"various":[77],"scales,":[79],"applies":[81],"series":[83],"of":[84,123,138,151,162],"filters":[85],"progressively":[87],"captures":[88],"features":[89],"coarse":[91],"fine":[93],"details":[94],"within":[95],"each":[96],"CT":[97],"slice":[98],"that":[99],"may":[100],"not":[101],"be":[102],"apparent":[103],"when":[104],"analyzed":[105],"at":[106],"single":[108],"resolution.":[109],"Such":[110],"an":[111,159],"approach":[112],"allows":[113],"more":[116],"discriminating":[117],"feature":[118],"representation,":[119],"improving":[120],"richness":[122],"available":[124],"information":[125],"analysis":[127],"reducing":[129],"reliance":[131],"on":[132,158],"large":[133],"datasets.":[134],"Addressing":[135],"limited":[139],"medical":[140],"image":[141],"resources,":[142],"effectively":[144],"decreases":[145],"computational":[146],"complexity":[147],"via":[148],"use":[150],"its":[152],"evidence-based":[153],"mechanism.":[154],"Through":[155],"experiments":[156],"conducted":[157],"in-house":[160],"dataset":[161],"114":[163],"subjects,":[164],"demonstrated":[166],"marked":[168],"improvement":[169],"accuracy,":[172],"outperforming":[173],"traditional":[174],"methods":[175],"with":[176],"far":[177],"less":[178],"training":[179],"data.":[180]},"counts_by_year":[],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}
