{"id":"https://openalex.org/W7128793140","doi":"https://doi.org/10.1109/access.2026.3664454","title":"A Cross-Modal Adversarial Network for Alzheimer\u2019s Disease Diagnosis Using Unpaired MRI and PET Imaging","display_name":"A Cross-Modal Adversarial Network for Alzheimer\u2019s Disease Diagnosis Using Unpaired MRI and PET Imaging","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7128793140","doi":"https://doi.org/10.1109/access.2026.3664454"},"language":null,"primary_location":{"id":"doi:10.1109/access.2026.3664454","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3664454","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3664454","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100609321","display_name":"Xing Wang","orcid":"https://orcid.org/0000-0001-9296-9043"},"institutions":[{"id":"https://openalex.org/I4210088165","display_name":"Anhui Sanlian University","ror":"https://ror.org/012sfmg27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210088165"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xing Wang","raw_affiliation_strings":["College of Modern Health and Wellness Industry, Anhui Sanlian University, Hefei, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Modern Health and Wellness Industry, Anhui Sanlian University, Hefei, China","institution_ids":["https://openalex.org/I4210088165"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hongxiang Xu","orcid":"https://orcid.org/0009-0007-4530-5317"},"institutions":[{"id":"https://openalex.org/I4210088165","display_name":"Anhui Sanlian University","ror":"https://ror.org/012sfmg27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210088165"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongxiang Xu","raw_affiliation_strings":["Anhui Sanlian Applied Traffic Technology Company Ltd., Hefei, China"],"raw_orcid":"https://orcid.org/0009-0007-4530-5317","affiliations":[{"raw_affiliation_string":"Anhui Sanlian Applied Traffic Technology Company Ltd., Hefei, China","institution_ids":["https://openalex.org/I4210088165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125890703","display_name":"Tao Gu","orcid":null},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Gu","raw_affiliation_strings":["College of Excellent Engineering, Hefei University of Technology, Hefei, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Excellent Engineering, Hefei University of Technology, Hefei, China","institution_ids":["https://openalex.org/I16365422"]}]},{"author_position":"last","author":{"id":null,"display_name":"Chengcheng Xu","orcid":"https://orcid.org/0009-0000-1795-5469"},"institutions":[{"id":"https://openalex.org/I18570673","display_name":"Guangxi University of Science and Technology","ror":"https://ror.org/02fj6b627","country_code":"CN","type":"education","lineage":["https://openalex.org/I18570673"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengcheng Xu","raw_affiliation_strings":["School of Science, Guangxi University of Science and Technology, Liuzhou, China"],"raw_orcid":"https://orcid.org/0009-0000-1795-5469","affiliations":[{"raw_affiliation_string":"School of Science, Guangxi University of Science and Technology, Liuzhou, China","institution_ids":["https://openalex.org/I18570673"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15573827,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"26935","last_page":"26952"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10009","display_name":"Dementia and Cognitive Impairment Research","score":0.219200000166893,"subfield":{"id":"https://openalex.org/subfields/2738","display_name":"Psychiatry and Mental health"},"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/T10009","display_name":"Dementia and Cognitive Impairment Research","score":0.219200000166893,"subfield":{"id":"https://openalex.org/subfields/2738","display_name":"Psychiatry and Mental health"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.12099999934434891,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.1071000024676323,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.6227999925613403},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6166999936103821},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5954999923706055},{"id":"https://openalex.org/keywords/positron-emission-tomography","display_name":"Positron emission tomography","score":0.5192999839782715},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.4945000112056732},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.48840001225471497},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.47369998693466187},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.46480000019073486}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7795000076293945},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7156000137329102},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.6227999925613403},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6166999936103821},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5954999923706055},{"id":"https://openalex.org/C2775842073","wikidata":"https://www.wikidata.org/wiki/Q208376","display_name":"Positron emission tomography","level":2,"score":0.5192999839782715},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.4945000112056732},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.48840001225471497},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.47369998693466187},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.46480000019073486},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4634000062942505},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.4611000120639801},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4519999921321869},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4092000126838684},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.36719998717308044},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.35429999232292175},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.35269999504089355},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35269999504089355},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32850000262260437},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3158999979496002},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C544519230","wikidata":"https://www.wikidata.org/wiki/Q32566","display_name":"Computed tomography","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2026.3664454","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3664454","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3664454","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3664454","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7729677557945251,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"and":[1,25,46,68,73,106,147],"early":[2],"diagnosis":[3,64,163],"of":[4,83,120,143,156,168],"Alzheimer\u2019s":[5],"disease":[6],"(AD)":[7],"is":[8,34,86],"critical":[9],"for":[10],"timely":[11],"intervention,":[12],"yet":[13],"existing":[14],"multimodal":[15,161],"approaches":[16],"typically":[17],"require":[18],"precisely":[19],"paired":[20],"magnetic":[21],"resonance":[22],"imaging":[23,122],"(MRI)":[24],"positron":[26],"emission":[27],"tomography":[28],"(PET)":[29],"scans,":[30],"a":[31,55,87],"condition":[32],"that":[33,60,134],"rarely":[35],"met":[36],"in":[37,175],"clinical":[38,177],"practice":[39],"due":[40],"to":[41,111,158],"missing":[42],"modalities,":[43],"protocol":[44],"heterogeneity,":[45],"logistical":[47],"constraints.":[48],"To":[49],"overcome":[50],"these":[51],"limitations,":[52],"we":[53],"propose":[54],"Cross-Modal":[56],"Adversarial":[57],"Network":[58,92],"(CMA-Net)":[59],"enables":[61],"effective":[62],"AD":[63,131,162],"using":[65],"unpaired":[66,130],"MRI":[67,146],"PET":[69],"images":[70],"during":[71],"training":[72],"supports":[74],"flexible":[75],"inference":[76],"with":[77,99],"either":[78],"modality":[79],"alone.":[80],"The":[81],"core":[82],"our":[84,135],"framework":[85],"Multimodal":[88],"Adaptive":[89],"Convolutional":[90],"Neural":[91],"(MA-CNN),":[93],"which":[94],"adopts":[95],"dual-branch":[96],"parallel":[97],"processing":[98],"modality-specific":[100],"Multi-Modal":[101],"Convolution":[102],"(MM":[103],"Conv)":[104],"blocks":[105],"integrates":[107],"multi-scale":[108],"attention":[109],"mechanisms":[110],"extract":[112],"discriminative":[113],"features":[114],"while":[115],"preserving":[116],"the":[117,154,165],"intrinsic":[118],"characteristics":[119],"each":[121],"modality.":[123],"Extensive":[124],"experiments":[125],"on":[126,145,149],"two":[127],"publicly":[128],"available,":[129],"datasets":[132],"demonstrate":[133],"method":[136],"achieves":[137],"state-of-the-art":[138],"performance,":[139],"yielding":[140],"classification":[141],"accuracies":[142],"96.27\u00b10.27%":[144],"94.92\u00b10.19%":[148],"PET.":[150],"These":[151],"results":[152],"underscore":[153],"potential":[155],"CMA-Net":[157],"facilitate":[159],"robust,":[160],"without":[164],"stringent":[166],"requirement":[167],"image":[169],"pairing,":[170],"thereby":[171],"enhancing":[172],"its":[173],"applicability":[174],"real-world":[176],"settings.":[178]},"counts_by_year":[],"updated_date":"2026-02-21T06:11:54.161237","created_date":"2026-02-14T00:00:00"}
