{"id":"https://openalex.org/W4313177998","doi":"https://doi.org/10.1109/icpr56361.2022.9956713","title":"Data-driven Latent Graph Structure Learning for Diagnosis of Alzheimer\u2019s Syndrome","display_name":"Data-driven Latent Graph Structure Learning for Diagnosis of Alzheimer\u2019s Syndrome","publication_year":2022,"publication_date":"2022-08-21","ids":{"openalex":"https://openalex.org/W4313177998","doi":"https://doi.org/10.1109/icpr56361.2022.9956713"},"language":"en","primary_location":{"id":"doi:10.1109/icpr56361.2022.9956713","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956713","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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/A5055189079","display_name":"Jianjia Wang","orcid":"https://orcid.org/0000-0003-1983-1632"},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianjia Wang","raw_affiliation_strings":["Shanghai University,School of Computer Engineering and Science, Shanghai Institute for Advanced Communication and Data Science","School of Computer Engineering and Science, Shanghai Institute for Advanced Communication and Data Science, Shanghai University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai University,School of Computer Engineering and Science, Shanghai Institute for Advanced Communication and Data Science","institution_ids":["https://openalex.org/I113940042"]},{"raw_affiliation_string":"School of Computer Engineering and Science, Shanghai Institute for Advanced Communication and Data Science, Shanghai University","institution_ids":["https://openalex.org/I113940042"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046886135","display_name":"Chong Wu","orcid":"https://orcid.org/0000-0002-8265-2760"},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chong Wu","raw_affiliation_strings":["Shanghai University,School of Computer Engineering and Science,Shanghai,China,200444"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai University,School of Computer Engineering and Science,Shanghai,China,200444","institution_ids":["https://openalex.org/I113940042"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I113940042"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18005449,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"106","issue":null,"first_page":"3138","last_page":"3144"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.998199999332428,"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"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.998199999332428,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.996399998664856,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6508474349975586},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.523280918598175},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4549219012260437},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.40018588304519653},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.36786311864852905},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.27206742763519287}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6508474349975586},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.523280918598175},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4549219012260437},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.40018588304519653},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.36786311864852905},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27206742763519287}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr56361.2022.9956713","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956713","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320325553","display_name":"Shanghai University","ror":"https://ror.org/006teas31"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1549386224","https://openalex.org/W1997339397","https://openalex.org/W2063858529","https://openalex.org/W2083278075","https://openalex.org/W2083826278","https://openalex.org/W2095491050","https://openalex.org/W2154851992","https://openalex.org/W2162142896","https://openalex.org/W2178225550","https://openalex.org/W2475109240","https://openalex.org/W2548228487","https://openalex.org/W2586350278","https://openalex.org/W2604525417","https://openalex.org/W2612872092","https://openalex.org/W2787337315","https://openalex.org/W2950089337","https://openalex.org/W2963794481","https://openalex.org/W2979750740","https://openalex.org/W3005922524","https://openalex.org/W3022335126","https://openalex.org/W3080253043","https://openalex.org/W3081203761","https://openalex.org/W3092864030","https://openalex.org/W3101784999","https://openalex.org/W3104097132","https://openalex.org/W3111514436","https://openalex.org/W3128461917","https://openalex.org/W3135138557","https://openalex.org/W3157999218","https://openalex.org/W3169450514","https://openalex.org/W3200529270","https://openalex.org/W3213924621","https://openalex.org/W4295750005","https://openalex.org/W4297733535","https://openalex.org/W4327873091","https://openalex.org/W6730091202","https://openalex.org/W6747904511","https://openalex.org/W6748320467","https://openalex.org/W6773773549","https://openalex.org/W6776909485","https://openalex.org/W6784273150","https://openalex.org/W6789531297","https://openalex.org/W6791375057"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W4402327032","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Complex":[0],"systems":[1],"often":[2],"have":[3],"a":[4,26,86],"latent":[5],"graph":[6,11,32,50,83,101,115],"structure.":[7],"Studying":[8],"the":[9,18,35,94,97,100,106,110,113,120,126,131,135,141],"underlying":[10],"structure":[12,116],"will":[13],"help":[14],"us":[15],"to":[16,29,40,92,129],"analyze":[17],"mechanisms":[19],"of":[20,65,76,96,109,134],"complex":[21],"phenomena.":[22],"However,":[23],"it":[24],"is":[25,69,90,117],"challenging":[27],"problem":[28],"learn":[30],"effective":[31],"structures":[33,102],"from":[34,105],"data":[36,64,123],"and":[37,85,125],"apply":[38],"them":[39],"downstream":[41],"tasks.":[42],"In":[43,138],"this":[44],"paper,":[45],"we":[46],"propose":[47],"an":[48],"end-to-end":[49],"learning":[51],"approach":[52,146],"for":[53],"Alzheimer\u2019s":[54],"syndrome":[55],"diagnosis":[56],"based":[57],"on":[58],"functional":[59],"magnetic":[60],"resonance":[61],"imaging":[62],"(fMRI)":[63],"brain":[66,78,136],"regions,":[67],"which":[68],"completely":[70],"data-driven.":[71],"The":[72],"interactions":[73],"between":[74],"time-series":[75,122],"each":[77],"region":[79],"are":[80,103],"represented":[81],"as":[82],"structures,":[84],"multi-head":[87],"attention":[88],"mechanism":[89],"used":[91],"update":[93],"representations":[95],"nodes.":[98],"Then,":[99],"obtained":[104],"feature":[107],"sampling":[108],"edges.":[111],"Finally,":[112],"learned":[114],"combined":[118],"with":[119,140],"left-out":[121],"features":[124],"node":[127],"prior":[128],"completing":[130],"classification":[132,149],"task":[133],"network.":[137],"comparison":[139],"latest":[142],"research":[143],"methods,":[144],"our":[145],"achieves":[147],"higher":[148],"accuracy.":[150]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
