{"id":"https://openalex.org/W2611452721","doi":"https://doi.org/10.1109/tip.2018.2799324","title":"Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network","display_name":"Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network","publication_year":2018,"publication_date":"2018-01-29","ids":{"openalex":"https://openalex.org/W2611452721","doi":"https://doi.org/10.1109/tip.2018.2799324","mag":"2611452721","pmid":"https://pubmed.ncbi.nlm.nih.gov/29470171"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2018.2799324","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2018.2799324","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1705.00727","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xiangyong Cao","orcid":"https://orcid.org/0000-0001-7912-3457"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiangyong Cao","raw_affiliation_strings":["School of Mathematics and Statistics, Xi\u2019an Jiaotong University, Xi\u2019an, China"],"raw_orcid":"https://orcid.org/0000-0001-7912-3457","affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University, Xi\u2019an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Feng Zhou","orcid":"https://orcid.org/0000-0002-1514-7393"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Zhou","raw_affiliation_strings":["National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an, China"],"raw_orcid":"https://orcid.org/0000-0002-1514-7393","affiliations":[{"raw_affiliation_string":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Lin Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I120250893","display_name":"New York University Abu Dhabi","ror":"https://ror.org/00e5k0821","country_code":"AE","type":"education","lineage":["https://openalex.org/I120250893","https://openalex.org/I57206974"]}],"countries":["AE"],"is_corresponding":false,"raw_author_name":"Lin Xu","raw_affiliation_strings":["NYU Multimedia and Visual Computing Laboratory, New York University, Abu Dhabi, United Arab Emirates"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NYU Multimedia and Visual Computing Laboratory, New York University, Abu Dhabi, United Arab Emirates","institution_ids":["https://openalex.org/I120250893"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Deyu Meng","orcid":"https://orcid.org/0000-0002-1294-8283"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Deyu Meng","raw_affiliation_strings":["School of Mathematics and Statistics, Xi\u2019an Jiaotong University, Xi\u2019an, China"],"raw_orcid":"https://orcid.org/0000-0002-1294-8283","affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University, Xi\u2019an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zongben Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zongben Xu","raw_affiliation_strings":["School of Mathematics and Statistics, Xi\u2019an Jiaotong University, Xi\u2019an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University, Xi\u2019an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":null,"display_name":"John Paisley","orcid":null},"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":"John Paisley","raw_affiliation_strings":["Department of Electrical Engineering, Data Science Institute, Columbia University, New York, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Data Science Institute, Columbia University, New York, NY, USA","institution_ids":["https://openalex.org/I78577930"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I87445476"],"apc_list":null,"apc_paid":null,"fwci":32.4001,"has_fulltext":false,"cited_by_count":297,"citation_normalized_percentile":{"value":0.99726776,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"27","issue":"5","first_page":"2354","last_page":"2367"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9901999831199646,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9901999831199646,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.0008999999845400453,"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.000699999975040555,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.724399983882904},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6848000288009644},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5853999853134155},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.579800009727478},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.5702000260353088},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.503000020980835},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4681999981403351},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4426000118255615},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.42500001192092896},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4097999930381775}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.724399983882904},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.707099974155426},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6848000288009644},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.608299970626831},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5853999853134155},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.579800009727478},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.5702000260353088},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.503000020980835},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4681999981403351},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4426000118255615},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.42500001192092896},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4097999930381775},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.36660000681877136},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.34869998693466187},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.34630000591278076},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.31790000200271606},{"id":"https://openalex.org/C130402806","wikidata":"https://www.wikidata.org/wiki/Q5361768","display_name":"Random field","level":2,"score":0.31520000100135803},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C102634674","wikidata":"https://www.wikidata.org/wiki/Q868473","display_name":"Smoothness","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3021000027656555},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.28049999475479126},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.26980000734329224},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2678000032901764},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.2630000114440918},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2624000012874603},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2556999921798706},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25540000200271606},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.2542000114917755}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tip.2018.2799324","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2018.2799324","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Image Processing","raw_type":"journal-article"},{"id":"pmid:29470171","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/29470171","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null},{"id":"pmh:oai:arXiv.org:1705.00727","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1705.00727","pdf_url":"https://arxiv.org/pdf/1705.00727","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1705.00727","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1705.00727","pdf_url":"https://arxiv.org/pdf/1705.00727","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1139308136","display_name":null,"funder_award_id":"2016013581","funder_id":"https://openalex.org/F4320322725","funder_display_name":"China Scholarship Council"},{"id":"https://openalex.org/G3383948137","display_name":null,"funder_award_id":"61603292","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4676737233","display_name":null,"funder_award_id":"61603235","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5234250448","display_name":null,"funder_award_id":"11690011","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6387986177","display_name":null,"funder_award_id":"61721002","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7324242277","display_name":null,"funder_award_id":"91330204","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7926885967","display_name":null,"funder_award_id":"61661166011","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8091708645","display_name":null,"funder_award_id":"11131006","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/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W1521436688","https://openalex.org/W1528463600","https://openalex.org/W1588948804","https://openalex.org/W1634257591","https://openalex.org/W1885185971","https://openalex.org/W1897878572","https://openalex.org/W1909515874","https://openalex.org/W1950365613","https://openalex.org/W1978187446","https://openalex.org/W1979158807","https://openalex.org/W1983364832","https://openalex.org/W1997478538","https://openalex.org/W1997565609","https://openalex.org/W2001298023","https://openalex.org/W2004104348","https://openalex.org/W2008233110","https://openalex.org/W2010735452","https://openalex.org/W2011745746","https://openalex.org/W2029316659","https://openalex.org/W2056621966","https://openalex.org/W2069143585","https://openalex.org/W2090424610","https://openalex.org/W2097915756","https://openalex.org/W2099129687","https://openalex.org/W2099608685","https://openalex.org/W2101309634","https://openalex.org/W2102236241","https://openalex.org/W2102249220","https://openalex.org/W2103094532","https://openalex.org/W2104269704","https://openalex.org/W2113464037","https://openalex.org/W2113754220","https://openalex.org/W2114220616","https://openalex.org/W2114819256","https://openalex.org/W2131697388","https://openalex.org/W2131725398","https://openalex.org/W2131864940","https://openalex.org/W2136251662","https://openalex.org/W2143516773","https://openalex.org/W2144966944","https://openalex.org/W2156982777","https://openalex.org/W2158400785","https://openalex.org/W2159070926","https://openalex.org/W2162698522","https://openalex.org/W2164437025","https://openalex.org/W2164918853","https://openalex.org/W2166723180","https://openalex.org/W2169415915","https://openalex.org/W2169551590","https://openalex.org/W2171171329","https://openalex.org/W2186541020","https://openalex.org/W2342626288","https://openalex.org/W2464755555","https://openalex.org/W2509784253","https://openalex.org/W2555840851","https://openalex.org/W2577238056","https://openalex.org/W2766736793","https://openalex.org/W4240485910","https://openalex.org/W6674330103","https://openalex.org/W6676338569","https://openalex.org/W6679349572","https://openalex.org/W6681686951","https://openalex.org/W6684191040"],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,21,33,39,51,69,124],"new":[4],"supervised":[5],"classification":[6,30,107],"algorithm":[7],"for":[8],"remotely":[9],"sensed":[10],"hyperspectral":[11],"image":[12],"(HSI)":[13],"which":[14],"integrates":[15],"spectral":[16],"and":[17,87,117],"spatial":[18,59,62,70],"information":[19,63],"in":[20,123],"unified":[22],"Bayesian":[23,34],"framework.":[24],"First,":[25],"we":[26,37,77],"formulate":[27],"the":[28,46,58,74,80,89,102,106],"HSI":[29,120],"problem":[31],"from":[32],"perspective.":[35],"Then,":[36],"adopt":[38],"convolutional":[40],"neural":[41],"network":[42],"(CNN)":[43],"to":[44,55],"learn":[45],"posterior":[47],"class":[48,90],"distributions":[49],"using":[50,83,96],"patch-wise":[52],"training":[53],"strategy":[54],"better":[56,110],"use":[57],"information.":[60],"Next,":[61],"is":[64],"further":[65],"considered":[66],"by":[67],"placing":[68],"smoothness":[71],"prior":[72],"on":[73,112],"labels.":[75],"Finally,":[76],"iteratively":[78],"update":[79,88],"CNN":[81],"parameters":[82],"stochastic":[84],"gradient":[85],"decent":[86],"labels":[91],"of":[92,126],"all":[93],"pixel":[94],"vectors":[95],"-expansion":[97],"min-cut-based":[98],"algorithm.":[99],"Compared":[100],"with":[101],"other":[103],"state-of-the-art":[104],"methods,":[105],"method":[108],"achieves":[109],"performance":[111],"one":[113],"synthetic":[114],"data":[115,121],"set":[116],"two":[118],"benchmark":[119],"sets":[122],"number":[125],"experimental":[127],"settings.":[128]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":25},{"year":2024,"cited_by_count":22},{"year":2023,"cited_by_count":40},{"year":2022,"cited_by_count":53},{"year":2021,"cited_by_count":62},{"year":2020,"cited_by_count":40},{"year":2019,"cited_by_count":40},{"year":2018,"cited_by_count":13}],"updated_date":"2026-05-12T08:28:47.272897","created_date":"2017-05-12T00:00:00"}