{"id":"https://openalex.org/W3103712661","doi":"https://doi.org/10.1109/access.2020.3037107","title":"Anatomical Landmarks and DAG Network Learning for Alzheimer\u2019s Disease Diagnosis","display_name":"Anatomical Landmarks and DAG Network Learning for Alzheimer\u2019s Disease Diagnosis","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3103712661","doi":"https://doi.org/10.1109/access.2020.3037107","mag":"3103712661"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.3037107","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3037107","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09253576.pdf","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://ieeexplore.ieee.org/ielx7/6287639/8948470/09253576.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101078085","display_name":"Zhu Tian","orcid":null},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tian Zhu","raw_affiliation_strings":["Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan, China","institution_ids":["https://openalex.org/I28006308"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075395351","display_name":"Chongfeng Cao","orcid":null},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]},{"id":"https://openalex.org/I4210106451","display_name":"Jinan Central Hospital","ror":"https://ror.org/01fr19c68","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210106451"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chongfeng Cao","raw_affiliation_strings":["Department of Critical Care Medicine, Jinan Central Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Critical Care Medicine, Jinan Central Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455","https://openalex.org/I4210106451"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102854362","display_name":"Zhishun Wang","orcid":"https://orcid.org/0000-0003-3739-4886"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhishun Wang","raw_affiliation_strings":["Department of Psychiatry, Columbia University, New York, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Psychiatry, Columbia University, New York, NY, USA","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075924413","display_name":"Guangrun Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210161528","display_name":"Qilu Hospital of Shandong University","ror":"https://ror.org/056ef9489","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210161528"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangrun Xu","raw_affiliation_strings":["Department of Neurology, Qilu Hospital of Shandong University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Neurology, Qilu Hospital of Shandong University, Jinan, China","institution_ids":["https://openalex.org/I4210161528"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053899187","display_name":"Jianping Qiao","orcid":"https://orcid.org/0000-0001-8910-7813"},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianping Qiao","raw_affiliation_strings":["Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan, China"],"raw_orcid":"https://orcid.org/0000-0001-8910-7813","affiliations":[{"raw_affiliation_string":"Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan, China","institution_ids":["https://openalex.org/I28006308"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"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":1.1236,"has_fulltext":true,"cited_by_count":21,"citation_normalized_percentile":{"value":0.78872407,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"8","issue":null,"first_page":"206063","last_page":"206073"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9918000102043152,"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"}},"topics":[{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9918000102043152,"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"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9837999939918518,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9807000160217285,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.8065555095672607},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7707419395446777},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7408287525177002},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.6921041011810303},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.659285843372345},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5956737995147705},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5501022934913635},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5402870178222656},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.48355957865715027},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.46931859850883484},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4634203016757965},{"id":"https://openalex.org/keywords/computer-aided-diagnosis","display_name":"Computer-aided diagnosis","score":0.46039336919784546},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45146891474723816},{"id":"https://openalex.org/keywords/voxel","display_name":"Voxel","score":0.444953590631485},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4291646480560303},{"id":"https://openalex.org/keywords/directed-acyclic-graph","display_name":"Directed acyclic graph","score":0.4264097809791565},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4217483401298523},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.33341294527053833},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.18219804763793945}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8065555095672607},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7707419395446777},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7408287525177002},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6921041011810303},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.659285843372345},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5956737995147705},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5501022934913635},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5402870178222656},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.48355957865715027},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.46931859850883484},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4634203016757965},{"id":"https://openalex.org/C2779549770","wikidata":"https://www.wikidata.org/wiki/Q1122413","display_name":"Computer-aided diagnosis","level":2,"score":0.46039336919784546},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45146891474723816},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.444953590631485},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4291646480560303},{"id":"https://openalex.org/C74197172","wikidata":"https://www.wikidata.org/wiki/Q1195339","display_name":"Directed acyclic graph","level":2,"score":0.4264097809791565},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4217483401298523},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33341294527053833},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.18219804763793945},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.3037107","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3037107","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09253576.pdf","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"},{"id":"pmh:oai:doaj.org/article:028b0696e91f45109a71565c03223839","is_oa":true,"landing_page_url":"https://doaj.org/article/028b0696e91f45109a71565c03223839","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 206063-206073 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.3037107","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3037107","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09253576.pdf","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":[{"score":0.7400000095367432,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G1858570305","display_name":null,"funder_award_id":"2016M602182","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G1941901634","display_name":null,"funder_award_id":"ZR2016FQ04","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"},{"id":"https://openalex.org/G2537253255","display_name":null,"funder_award_id":"2016GGX101009","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"},{"id":"https://openalex.org/G5154929720","display_name":null,"funder_award_id":"2017CXGC1504","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5968571716","display_name":null,"funder_award_id":"61603225","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"},{"id":"https://openalex.org/F4320324174","display_name":"Natural Science Foundation of Shandong Province","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3103712661.pdf","grobid_xml":"https://content.openalex.org/works/W3103712661.grobid-xml"},"referenced_works_count":64,"referenced_works":["https://openalex.org/W1510385670","https://openalex.org/W1522301498","https://openalex.org/W1602771322","https://openalex.org/W1679736479","https://openalex.org/W1810343284","https://openalex.org/W1976526581","https://openalex.org/W1978832801","https://openalex.org/W1989043803","https://openalex.org/W2030637554","https://openalex.org/W2030810279","https://openalex.org/W2038003677","https://openalex.org/W2043768383","https://openalex.org/W2054540100","https://openalex.org/W2058046532","https://openalex.org/W2065056664","https://openalex.org/W2077303669","https://openalex.org/W2086613190","https://openalex.org/W2086978209","https://openalex.org/W2096410114","https://openalex.org/W2110208125","https://openalex.org/W2119848633","https://openalex.org/W2137664016","https://openalex.org/W2138375704","https://openalex.org/W2146089088","https://openalex.org/W2153021580","https://openalex.org/W2153171432","https://openalex.org/W2154158661","https://openalex.org/W2154755614","https://openalex.org/W2157072800","https://openalex.org/W2187089797","https://openalex.org/W2192985207","https://openalex.org/W2289251924","https://openalex.org/W2463258885","https://openalex.org/W2473821704","https://openalex.org/W2474996166","https://openalex.org/W2475285134","https://openalex.org/W2524093654","https://openalex.org/W2559881210","https://openalex.org/W2572093290","https://openalex.org/W2614542815","https://openalex.org/W2733769715","https://openalex.org/W2746293221","https://openalex.org/W2765366332","https://openalex.org/W2783188875","https://openalex.org/W2888898379","https://openalex.org/W2896115045","https://openalex.org/W2899635607","https://openalex.org/W2901294913","https://openalex.org/W2901932536","https://openalex.org/W2903906898","https://openalex.org/W2906155095","https://openalex.org/W2923917921","https://openalex.org/W2945065837","https://openalex.org/W2946753127","https://openalex.org/W2946843602","https://openalex.org/W2947823562","https://openalex.org/W2964121744","https://openalex.org/W2988812296","https://openalex.org/W2998158849","https://openalex.org/W3000897208","https://openalex.org/W3014247165","https://openalex.org/W4230920194","https://openalex.org/W6631190155","https://openalex.org/W6637510049"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W4376166922","https://openalex.org/W4378510483","https://openalex.org/W3128011703"],"abstract_inverted_index":{"The":[0,16],"accurate":[1],"diagnosis":[2,11,27,93],"and":[3,36,80,111,117,152,160,170,184],"prediction":[4],"for":[5,91,202],"individuals":[6],"is":[7,64,123],"crucial":[8],"in":[9,67],"computer-aided":[10],"of":[12,25,44,61,94,102,131,144,174],"Alzheimer's":[13],"disease":[14],"(AD).":[15],"existing":[17],"structural":[18],"magnetic":[19],"resonance":[20],"imaging":[21],"based":[22,88],"classification":[23,89,168,195],"methods":[24,46,201],"AD":[26,95,203],"mainly":[28],"focus":[29],"on":[30,177],"the":[31,59,68,92,98,109,127,135,167,172,188,194,199],"voxel":[32],"level,":[33],"region":[34],"level":[35,38],"patch":[37],"morphological":[39,110],"pattern":[40],"analysis.":[41,113],"However,":[42],"most":[43],"these":[45],"extract":[47,126],"features":[48,130,137,162],"with":[49,198],"high":[50],"dimension":[51],"which":[52,148],"may":[53],"lead":[54],"to":[55,125,165],"overfitting":[56],"problem.":[57],"Besides,":[58],"interaction":[60],"different":[62,145],"patches":[63,101],"not":[65],"considered":[66],"classifier":[69],"ensemble.":[70],"In":[71],"this":[72],"article,":[73],"we":[74],"propose":[75],"a":[76,115],"novel":[77],"anatomical":[78,99],"landmarks":[79],"directed":[81],"acyclic":[82],"graph":[83],"(DAG)":[84],"network":[85,122,146],"feature":[86,100,142],"learning":[87],"algorithm":[90],"individuals.":[96],"First,":[97],"gray":[103],"matter":[104],"image":[105,132],"are":[106,138,163],"identified":[107],"by":[108,140],"statistical":[112],"Second,":[114],"simple":[116],"efficient":[118],"DAG":[119],"convolutional":[120],"neural":[121],"proposed":[124,189],"discriminative":[128],"deep":[129,136,161],"representation.":[133],"Especially,":[134],"obtained":[139],"fusing":[141],"maps":[143],"levels":[147],"contain":[149],"semantic":[150],"high-level":[151],"high-resolution":[153],"low-level":[154],"features.":[155],"Finally,":[156],"support":[157],"vector":[158],"machine":[159],"utilized":[164],"construct":[166],"model":[169],"predict":[171],"individual":[173],"AD.":[175],"Experiments":[176],"three":[178],"public":[179],"datasets":[180],"including":[181],"ADNI-1,":[182],"ADNI-2":[183],"MIRIAD":[185],"demonstrate":[186],"that":[187],"method":[190],"can":[191],"effectively":[192],"improve":[193],"performance":[196],"compared":[197],"state-of-the-art":[200],"diagnosis.":[204]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-27T08:28:00.272161","created_date":"2025-10-10T00:00:00"}
