{"id":"https://openalex.org/W3081571895","doi":"https://doi.org/10.1109/med48518.2020.9183193","title":"Tumor Growth Modeling: State Estimation with Maximum Likelihood and Particle Filtering","display_name":"Tumor Growth Modeling: State Estimation with Maximum Likelihood and Particle Filtering","publication_year":2020,"publication_date":"2020-09-01","ids":{"openalex":"https://openalex.org/W3081571895","doi":"https://doi.org/10.1109/med48518.2020.9183193","mag":"3081571895"},"language":"en","primary_location":{"id":"doi:10.1109/med48518.2020.9183193","is_oa":false,"landing_page_url":"https://doi.org/10.1109/med48518.2020.9183193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 28th Mediterranean Conference on Control and Automation (MED)","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/A5031391055","display_name":"Spyridon Patmanidis","orcid":"https://orcid.org/0000-0003-2117-8038"},"institutions":[{"id":"https://openalex.org/I174458059","display_name":"National Technical University of Athens","ror":"https://ror.org/03cx6bg69","country_code":"GR","type":"education","lineage":["https://openalex.org/I174458059"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Spyridon Patmanidis","raw_affiliation_strings":["School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Polytechneiou 9, Zografou, Athens, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Polytechneiou 9, Zografou, Athens, Greece","institution_ids":["https://openalex.org/I174458059"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021825170","display_name":"Alexandros C. Charalampidis","orcid":"https://orcid.org/0000-0003-3260-3722"},"institutions":[{"id":"https://openalex.org/I4210100151","display_name":"Institut d'\u00c9lectronique et des Technologies du num\u00e9Rique","ror":"https://ror.org/013q33h79","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I28221208","https://openalex.org/I4210095849","https://openalex.org/I4210100151","https://openalex.org/I56067802","https://openalex.org/I97188460"]},{"id":"https://openalex.org/I4210107720","display_name":"CentraleSup\u00e9lec","ror":"https://ror.org/019tcpt25","country_code":"FR","type":"facility","lineage":["https://openalex.org/I277688954","https://openalex.org/I4210107720"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Alexandros C. Charalampidis","raw_affiliation_strings":["CentraleSup\u00e9lec, Automatic Control Group - IETR, Avenue de la Boulaie, Cesson-S\u00e9vign\u00e9, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CentraleSup\u00e9lec, Automatic Control Group - IETR, Avenue de la Boulaie, Cesson-S\u00e9vign\u00e9, France","institution_ids":["https://openalex.org/I4210100151","https://openalex.org/I4210107720"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031498207","display_name":"George P. Papavassilopoulos","orcid":null},"institutions":[{"id":"https://openalex.org/I174458059","display_name":"National Technical University of Athens","ror":"https://ror.org/03cx6bg69","country_code":"GR","type":"education","lineage":["https://openalex.org/I174458059"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"George P. Papavassilopoulos","raw_affiliation_strings":["School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Polytechneiou 9, Zografou, Athens, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Polytechneiou 9, Zografou, Athens, Greece","institution_ids":["https://openalex.org/I174458059"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09220831,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"50","issue":null,"first_page":"144","last_page":"149"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","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"}},"topics":[{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9915000200271606,"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/T11829","display_name":"Mathematical Biology Tumor Growth","score":0.9872000217437744,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6630246639251709},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6141671538352966},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6042680144309998},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.589103102684021},{"id":"https://openalex.org/keywords/cuda","display_name":"CUDA","score":0.5552812218666077},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.5470137000083923},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5255961418151855},{"id":"https://openalex.org/keywords/gompertz-function","display_name":"Gompertz function","score":0.5048118829727173},{"id":"https://openalex.org/keywords/standard-deviation","display_name":"Standard deviation","score":0.43724098801612854},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.33202415704727173},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.32572728395462036},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.29385387897491455},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.09018683433532715}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6630246639251709},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6141671538352966},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6042680144309998},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.589103102684021},{"id":"https://openalex.org/C2778119891","wikidata":"https://www.wikidata.org/wiki/Q477690","display_name":"CUDA","level":2,"score":0.5552812218666077},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.5470137000083923},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5255961418151855},{"id":"https://openalex.org/C134463574","wikidata":"https://www.wikidata.org/wiki/Q1011785","display_name":"Gompertz function","level":2,"score":0.5048118829727173},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.43724098801612854},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.33202415704727173},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.32572728395462036},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29385387897491455},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.09018683433532715},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/med48518.2020.9183193","is_oa":false,"landing_page_url":"https://doi.org/10.1109/med48518.2020.9183193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 28th Mediterranean Conference on Control and Automation (MED)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W309601634","https://openalex.org/W1580750997","https://openalex.org/W1916448402","https://openalex.org/W1932940155","https://openalex.org/W1975116174","https://openalex.org/W2003967703","https://openalex.org/W2012976204","https://openalex.org/W2052811447","https://openalex.org/W2083382352","https://openalex.org/W2098613108","https://openalex.org/W2120749469","https://openalex.org/W2124078578","https://openalex.org/W2128981577","https://openalex.org/W2160337655","https://openalex.org/W2173690212","https://openalex.org/W2296355540","https://openalex.org/W2766144557","https://openalex.org/W2793314170","https://openalex.org/W2968523551","https://openalex.org/W2985083478","https://openalex.org/W6610866116","https://openalex.org/W6671560161"],"related_works":["https://openalex.org/W2927378857","https://openalex.org/W2801696468","https://openalex.org/W2406829934","https://openalex.org/W1824810860","https://openalex.org/W2185006999","https://openalex.org/W2162253570","https://openalex.org/W1583020711","https://openalex.org/W2138381686","https://openalex.org/W2010934810","https://openalex.org/W3144709167"],"abstract_inverted_index":{"In":[0],"this":[1],"study,":[2],"we":[3,89,161],"combined":[4],"the":[5,23,26,41,61,73,84,93,97,112,117,124,130,136,143,148,152,170,174,178,188],"Maximum":[6,153],"Likelihood":[7,154],"Estimator":[8,155],"from":[9],"our":[10],"previous":[11],"works":[12],"with":[13,53,157],"a":[14,35,193],"Sequential":[15],"Importance":[16],"Resampling":[17],"(SIR)":[18],"particle":[19],"filter":[20,43,63,114,180],"to":[21,46,58,72,80,164,191],"estimate":[22,116],"states":[24,119],"of":[25,96,106,169,187,197],"stochastic":[27],"Gompertz":[28,86],"tumor":[29,126],"growth":[30,127],"model.":[31,87],"We":[32],"also":[33],"implemented":[34],"parallel":[36,99,131],"version":[37],"in":[38,44,70,199],"CUDA":[39],"for":[40,103,177],"SIR":[42,62,113,159,179],"order":[45],"reduce":[47],"its":[48],"execution":[49,94,175],"time.":[50],"Extensive":[51],"simulations":[52],"synthetic":[54],"data":[55],"were":[56,162],"run":[57],"examine":[59],"whether":[60],"can":[64,115],"provide":[65],"more":[66,140],"accurate":[67,167],"state":[68],"estimates":[69,168],"respect":[71],"Normalized":[74],"Mean":[75],"Squared":[76],"Deviation":[77],"criterion":[78],"compared":[79,92],"those":[81],"provided":[82],"by":[83,184],"deterministic":[85],"Moreover,":[88],"monitored":[90],"and":[91,100],"time":[95,176],"SIR's":[98],"sequential":[101],"implementations":[102],"different":[104],"numbers":[105],"particles.":[107],"The":[108],"results":[109],"showed":[110],"that":[111,133,145],"system's":[118],"very":[120,166,194],"accurately,":[121],"even":[122],"at":[123],"early":[125],"stages.":[128],"Additionally,":[129],"implementation":[132,144],"ran":[134,146],"on":[135,147],"GPU":[137],"was":[138,181],"way":[139],"efficient":[141],"than":[142],"CPU.":[149],"By":[150],"combining":[151],"(MLE)":[156],"an":[158],"filter,":[160],"able":[163],"obtain":[165],"tumors'":[171],"volume.":[172],"Furthermore,":[173],"significantly":[182],"decreased":[183],"taking":[185],"advantage":[186],"GPUs":[189],"ability":[190],"perform":[192],"large":[195],"number":[196],"computations":[198],"parallel.":[200]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}