{"id":"https://openalex.org/W4391743024","doi":"https://doi.org/10.1109/imcom60618.2024.10418243","title":"Deep-RSIv2: An Efficient Content-Free Deep Learning Approach for Radiographs' Manufacturer and Model Identification","display_name":"Deep-RSIv2: An Efficient Content-Free Deep Learning Approach for Radiographs' Manufacturer and Model Identification","publication_year":2024,"publication_date":"2024-01-03","ids":{"openalex":"https://openalex.org/W4391743024","doi":"https://doi.org/10.1109/imcom60618.2024.10418243"},"language":"en","primary_location":{"id":"doi:10.1109/imcom60618.2024.10418243","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/imcom60618.2024.10418243","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM)","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/A5011566382","display_name":"Farid Ghareh Mohammadi","orcid":"https://orcid.org/0000-0002-5236-9059"},"institutions":[{"id":"https://openalex.org/I4210146710","display_name":"Mayo Clinic in Florida","ror":"https://ror.org/03zzw1w08","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1330342723","https://openalex.org/I4210146710"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Farid Ghareh Mohammadi","raw_affiliation_strings":["Center for Augmented Intelligence, Mayo Clinic,Department of Radiology,Jacksonville,FL,USA","Department of Radiology, Center for Augmented Intelligence, Mayo Clinic, Jacksonville, FL, USA"],"affiliations":[{"raw_affiliation_string":"Center for Augmented Intelligence, Mayo Clinic,Department of Radiology,Jacksonville,FL,USA","institution_ids":["https://openalex.org/I4210146710"]},{"raw_affiliation_string":"Department of Radiology, Center for Augmented Intelligence, Mayo Clinic, Jacksonville, FL, USA","institution_ids":["https://openalex.org/I4210146710"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069515302","display_name":"Ronnie Sebro","orcid":"https://orcid.org/0000-0001-7232-4416"},"institutions":[{"id":"https://openalex.org/I4210146710","display_name":"Mayo Clinic in Florida","ror":"https://ror.org/03zzw1w08","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1330342723","https://openalex.org/I4210146710"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ronnie Sebro","raw_affiliation_strings":["Center for Augmented Intelligence, Mayo Clinic,Department of Radiology,Jacksonville,FL,USA","Department of Radiology, Center for Augmented Intelligence, Mayo Clinic, Jacksonville, FL, USA"],"affiliations":[{"raw_affiliation_string":"Center for Augmented Intelligence, Mayo Clinic,Department of Radiology,Jacksonville,FL,USA","institution_ids":["https://openalex.org/I4210146710"]},{"raw_affiliation_string":"Department of Radiology, Center for Augmented Intelligence, Mayo Clinic, Jacksonville, FL, USA","institution_ids":["https://openalex.org/I4210146710"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5011566382"],"corresponding_institution_ids":["https://openalex.org/I4210146710"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0123669,"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":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.9998999834060669,"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/T10862","display_name":"AI in cancer detection","score":0.9722999930381775,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9326000213623047,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/radiography","display_name":"Radiography","score":0.7136688232421875},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7061100602149963},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6830394864082336},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5062838792800903},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.45071423053741455},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.42899876832962036},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3799581229686737},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3657884895801544},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.3268996477127075},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.32362204790115356},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.1088142991065979}],"concepts":[{"id":"https://openalex.org/C36454342","wikidata":"https://www.wikidata.org/wiki/Q245341","display_name":"Radiography","level":2,"score":0.7136688232421875},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7061100602149963},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6830394864082336},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5062838792800903},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.45071423053741455},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.42899876832962036},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3799581229686737},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3657884895801544},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3268996477127075},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.32362204790115356},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.1088142991065979},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/imcom60618.2024.10418243","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/imcom60618.2024.10418243","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM)","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":29,"referenced_works":["https://openalex.org/W1506864492","https://openalex.org/W1838866347","https://openalex.org/W1979317425","https://openalex.org/W2004373333","https://openalex.org/W2037023837","https://openalex.org/W2046180645","https://openalex.org/W2049771774","https://openalex.org/W2092669196","https://openalex.org/W2096754397","https://openalex.org/W2106773117","https://openalex.org/W2124695272","https://openalex.org/W2125382552","https://openalex.org/W2141701079","https://openalex.org/W2317971218","https://openalex.org/W2560266592","https://openalex.org/W2561004319","https://openalex.org/W2563375135","https://openalex.org/W2572787276","https://openalex.org/W2716368418","https://openalex.org/W2798117183","https://openalex.org/W2921213695","https://openalex.org/W3088481976","https://openalex.org/W3100415668","https://openalex.org/W4210572675","https://openalex.org/W4313479509","https://openalex.org/W4381188526","https://openalex.org/W4386158893","https://openalex.org/W4394598095","https://openalex.org/W4394629052"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4239306820","https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Artificial":[0],"Intelligence":[1],"(AI)-driven":[2],"digital":[3],"world":[4],"is":[5],"vulnerable":[6],"against":[7],"AI-based":[8],"threats,":[9],"particularly":[10],"deepfakes,":[11],"that":[12],"have":[13],"made":[14],"image":[15],"forgery":[16],"to":[17,91],"become":[18],"ubiquitous.":[19],"Digital":[20],"alteration,":[21],"or":[22],"AI-aided":[23],"generation":[24],"of":[25],"radiology":[26],"images":[27],"adversely":[28],"influence":[29],"patient":[30,67],"care.":[31],"We":[32,71],"collected":[33],"3875":[34],"patients(<tex":[35],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[36,44,55],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\mathrm{n}=4125$</tex>":[37],"radiographs),":[38],"including":[39],"chest":[40],"(1509":[41],"patients,":[42,53],"<tex":[43,54],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\mathrm{n}=3353$</tex>":[45],"radiographs,":[46,57],"mean":[47,58],"age=64.99,female:63%)":[48],"and":[49,104],"thoracic":[50,100,110],"spine":[51],"(2366":[52],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\mathrm{n}=8644$</tex>":[56],"age=67,":[59],"female:":[60],"33%).":[61],"Data":[62],"were":[63],"randomly":[64],"divided":[65],"by":[66,85,94],"into":[68],"training/validation(80%),":[69],"test(20%).":[70],"develop":[72],"Deep-RSIv2,":[73],"an":[74],"efficient":[75],"content-free":[76],"deep-learning":[77],"framework":[78],"using":[79],"light":[80],"three":[81],"convolutional":[82],"layers":[83,90],"followed":[84],"fully":[86],"connected":[87],"neural":[88],"network":[89],"authenticate":[92],"radiographs":[93],"manufacturers":[95],"with":[96,106],"AUC=[chest(3":[97],"classes):":[98,102,108,112],"95.85%,":[99],"spine(3":[101],"93.87%];":[103],"models":[105],"AUC=[chest(5":[107],"93.87%,":[109],"spine(4":[111],"91.36%].":[113]},"counts_by_year":[],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}
