{"id":"https://openalex.org/W2890530628","doi":"https://doi.org/10.1109/access.2018.2869780","title":"A Feasibility of Respiration Prediction Based on Deep Bi-LSTM for Real-Time Tumor Tracking","display_name":"A Feasibility of Respiration Prediction Based on Deep Bi-LSTM for Real-Time Tumor Tracking","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2890530628","doi":"https://doi.org/10.1109/access.2018.2869780","mag":"2890530628"},"language":"en","primary_location":{"id":"doi:10.1109/access.2018.2869780","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2018.2869780","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.2018.2869780","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5104753591","display_name":"Ran Wang","orcid":"https://orcid.org/0000-0001-9795-9450"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ran Wang","raw_affiliation_strings":["University of Chinese Academy of Sciences or Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-9795-9450","affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences or Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056242195","display_name":"Xiaokun Liang","orcid":"https://orcid.org/0000-0002-1207-5726"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaokun Liang","raw_affiliation_strings":["University of Chinese Academy of Sciences or Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-1207-5726","affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences or Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062161005","display_name":"Xuanyu Zhu","orcid":"https://orcid.org/0000-0001-9140-9992"},"institutions":[{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuanyu Zhu","raw_affiliation_strings":["Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, Shenzhen, China","institution_ids":["https://openalex.org/I4210145761"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086187557","display_name":"Yaoqin Xie","orcid":"https://orcid.org/0000-0002-1412-2354"},"institutions":[{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaoqin Xie","raw_affiliation_strings":["Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, Shenzhen, China","institution_ids":["https://openalex.org/I4210145761"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":7.61,"has_fulltext":false,"cited_by_count":89,"citation_normalized_percentile":{"value":0.9784504,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"6","issue":null,"first_page":"51262","last_page":"51268"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10358","display_name":"Advanced Radiotherapy Techniques","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/3108","display_name":"Radiation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10358","display_name":"Advanced Radiotherapy Techniques","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/3108","display_name":"Radiation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9811999797821045,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.7491370439529419},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.73072749376297},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5719496607780457},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.5143809914588928},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.502865731716156},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4968908131122589},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.4750889837741852},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.4314959943294525},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.42719507217407227},{"id":"https://openalex.org/keywords/mean-absolute-percentage-error","display_name":"Mean absolute percentage error","score":0.4198855757713318},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.414533793926239},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.28229546546936035},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1397821605205536},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13843178749084473}],"concepts":[{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.7491370439529419},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.73072749376297},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5719496607780457},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.5143809914588928},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.502865731716156},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4968908131122589},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.4750889837741852},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.4314959943294525},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.42719507217407227},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.4198855757713318},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.414533793926239},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.28229546546936035},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1397821605205536},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13843178749084473},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2018.2869780","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2018.2869780","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"},{"id":"pmh:oai:doaj.org/article:4d136c4d56e54c928f8462235dc257c7","is_oa":true,"landing_page_url":"https://doaj.org/article/4d136c4d56e54c928f8462235dc257c7","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 6, Pp 51262-51268 (2018)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2018.2869780","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2018.2869780","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":[{"id":"https://metadata.un.org/sdg/3","score":0.7599999904632568,"display_name":"Good health and well-being"}],"awards":[{"id":"https://openalex.org/G1161121357","display_name":null,"funder_award_id":"2016YFC0105102","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G207616250","display_name":null,"funder_award_id":"81871433","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G978049144","display_name":null,"funder_award_id":"JSGG20160229203812944","funder_id":"https://openalex.org/F4320335770","funder_display_name":"Shenzhen Technical Project"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320327583","display_name":"Beijing Center for Mathematics and Information Interdisciplinary Sciences","ror":null},{"id":"https://openalex.org/F4320335770","display_name":"Shenzhen Technical Project","ror":null},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W60495656","https://openalex.org/W179875071","https://openalex.org/W581956982","https://openalex.org/W961558816","https://openalex.org/W1565073233","https://openalex.org/W1971873504","https://openalex.org/W1978913152","https://openalex.org/W1988296725","https://openalex.org/W1989130706","https://openalex.org/W1991301669","https://openalex.org/W2009316899","https://openalex.org/W2016210396","https://openalex.org/W2017695843","https://openalex.org/W2023514351","https://openalex.org/W2027226834","https://openalex.org/W2031833011","https://openalex.org/W2033042197","https://openalex.org/W2036558320","https://openalex.org/W2039240409","https://openalex.org/W2041430895","https://openalex.org/W2050049213","https://openalex.org/W2052499560","https://openalex.org/W2054211387","https://openalex.org/W2059804518","https://openalex.org/W2062096738","https://openalex.org/W2064675550","https://openalex.org/W2065385932","https://openalex.org/W2069926016","https://openalex.org/W2071042777","https://openalex.org/W2076127024","https://openalex.org/W2079735306","https://openalex.org/W2085990623","https://openalex.org/W2091812280","https://openalex.org/W2094265705","https://openalex.org/W2122090544","https://openalex.org/W2144499799","https://openalex.org/W2158899491","https://openalex.org/W2166064220","https://openalex.org/W2323852092","https://openalex.org/W2413885457","https://openalex.org/W2485197820","https://openalex.org/W2528514028","https://openalex.org/W2731782018","https://openalex.org/W2952230511","https://openalex.org/W6602500974","https://openalex.org/W6607333740","https://openalex.org/W6616837769","https://openalex.org/W6631439307","https://openalex.org/W6634037989","https://openalex.org/W6643083873","https://openalex.org/W6667628739","https://openalex.org/W6683738474","https://openalex.org/W6700632408"],"related_works":["https://openalex.org/W4307751489","https://openalex.org/W2076543106","https://openalex.org/W4382118046","https://openalex.org/W2523437662","https://openalex.org/W89844371","https://openalex.org/W2019891950","https://openalex.org/W2085842814","https://openalex.org/W4286643620","https://openalex.org/W4387048144","https://openalex.org/W4386859505"],"abstract_inverted_index":{"In":[0],"radiotherapy,":[1,230],"the":[2,24,62,126,137,177,211,240],"position":[3],"of":[4,15,19,27,32,50,65,128,145,149,159,171,180,213],"thoracic-abdominal":[5,16,33,229],"tumor":[6,34,98],"is":[7,18,36,53,83,103,185,232],"changing":[8],"due":[9],"to":[10,38,54,60,85,105,124,221],"respiratory":[11,100],"motion.":[12],"Real-time":[13],"tracking":[14,223],"tumors":[17],"great":[20,134],"significance":[21],"in":[22,43,136,228,239],"improving":[23],"treatment":[25,63],"effect":[26,64],"radiotherapy.":[28,66],"The":[29,48,216],"accurate":[30],"prediction":[31,58,178],"motion":[35,88,101],"required":[37],"compensate":[39],"for":[40,89,236],"system":[41],"latency":[42,91,162,212],"image-guided":[44],"adaptive":[45,203],"radiotherapy":[46],"systems.":[47],"purpose":[49],"this":[51],"paper":[52],"identify":[55],"an":[56],"optimal":[57],"model":[59,196],"improve":[61,222],"A":[67],"seven-layer":[68],"bidirectional":[69],"long":[70,141],"short":[71],"term":[72],"memory":[73],"(Deep":[74],"Bi-LSTM)":[75],"and":[76,117,152,197,205,225,234],"one":[77],"output":[78],"layer":[79],"deep":[80],"neural":[81,208],"network":[82,209],"proposed":[84,182],"predict":[86],"respiration":[87],"a":[90],"about":[92,163,186,198],"400":[93,164,214],"ms.":[94,215],"103":[95],"malignant":[96],"lung":[97],"patients'":[99],"data":[102],"used":[104],"train":[106],"model.":[107],"Mean":[108],"absolute":[109],"error":[110,115,121,157],"(MAE),":[111],"root":[112,154],"mean":[113,119,155],"square":[114,120,156],"(RMSE),":[116],"normalized":[118,153],"are":[122,166],"introduced":[123],"evaluate":[125],"performance":[127,135],"predictive":[129,169],"results.":[130],"Deep":[131,172,183],"Bi-LSTM":[132,184],"has":[133],"cases":[138],"with":[139,161],"relative":[140],"latency,":[142],"average":[143,195],"MAE":[144],"0.074":[146],"mm,":[147,151],"RMSE":[148],"0.097":[150],"(nRMSE)":[158],"0.081":[160],"ms":[165],"obtained":[167],"from":[168],"results":[170],"Bi-LSTM.":[173],"It":[174],"demonstrates":[175],"that":[176],"accuracy":[179,224],"our":[181],"five":[187],"times":[188,200],"better":[189,201],"than":[190,202],"traditional":[191],"autoregressive":[192],"integrated":[193],"moving":[194],"three":[199],"boosting":[204],"multi-layer":[206],"perceptron":[207],"when":[210],"method":[217],"can":[218],"be":[219],"applied":[220],"increase":[226],"efficiency":[227],"which":[231],"practical":[233],"attractive":[235],"clinical":[237],"application":[238],"near":[241],"future.":[242]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":15},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}