{"id":"https://openalex.org/W4394715556","doi":"https://doi.org/10.3390/s24082452","title":"Influence on Sample Determination for Deep Learning Electromagnetic Tomography","display_name":"Influence on Sample Determination for Deep Learning Electromagnetic Tomography","publication_year":2024,"publication_date":"2024-04-11","ids":{"openalex":"https://openalex.org/W4394715556","doi":"https://doi.org/10.3390/s24082452","pmid":"https://pubmed.ncbi.nlm.nih.gov/38676069"},"language":"en","primary_location":{"id":"doi:10.3390/s24082452","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s24082452","pdf_url":"https://www.mdpi.com/1424-8220/24/8/2452/pdf?version=1712839373","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/24/8/2452/pdf?version=1712839373","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100627240","display_name":"Pengfei Zhao","orcid":"https://orcid.org/0000-0002-5649-7998"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengfei Zhao","raw_affiliation_strings":["The School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China"],"raw_orcid":"https://orcid.org/0000-0002-5649-7998","affiliations":[{"raw_affiliation_string":"The School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100349461","display_name":"Ze Liu","orcid":"https://orcid.org/0000-0002-7478-435X"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ze Liu","raw_affiliation_strings":["The School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China"],"raw_orcid":"https://orcid.org/0000-0002-7478-435X","affiliations":[{"raw_affiliation_string":"The School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100349461"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":0.9556,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.73955568,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"24","issue":"8","first_page":"2452","last_page":"2452"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11778","display_name":"Electrical and Bioimpedance Tomography","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T11778","display_name":"Electrical and Bioimpedance Tomography","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T10572","display_name":"Geophysical and Geoelectrical Methods","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9779000282287598,"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/sample","display_name":"Sample (material)","score":0.6367716193199158},{"id":"https://openalex.org/keywords/tomography","display_name":"Tomography","score":0.5305578708648682},{"id":"https://openalex.org/keywords/medical-physics","display_name":"Medical physics","score":0.4712063670158386},{"id":"https://openalex.org/keywords/computed-tomography","display_name":"Computed tomography","score":0.4102400243282318},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.38355088233947754},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34353572130203247},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2788497805595398},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.17170271277427673},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.1346282958984375},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.10910084843635559}],"concepts":[{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.6367716193199158},{"id":"https://openalex.org/C163716698","wikidata":"https://www.wikidata.org/wiki/Q841267","display_name":"Tomography","level":2,"score":0.5305578708648682},{"id":"https://openalex.org/C19527891","wikidata":"https://www.wikidata.org/wiki/Q1120908","display_name":"Medical physics","level":1,"score":0.4712063670158386},{"id":"https://openalex.org/C544519230","wikidata":"https://www.wikidata.org/wiki/Q32566","display_name":"Computed tomography","level":2,"score":0.4102400243282318},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.38355088233947754},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34353572130203247},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2788497805595398},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.17170271277427673},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.1346282958984375},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.10910084843635559}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/s24082452","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s24082452","pdf_url":"https://www.mdpi.com/1424-8220/24/8/2452/pdf?version=1712839373","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:38676069","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38676069","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":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:11054014","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11054014","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11054014/pdf/sensors-24-02452.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:e89befa0cede47c8aa323be08420c581","is_oa":false,"landing_page_url":"https://doaj.org/article/e89befa0cede47c8aa323be08420c581","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 24, Iss 8, p 2452 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/s24082452","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s24082452","pdf_url":"https://www.mdpi.com/1424-8220/24/8/2452/pdf?version=1712839373","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.6399999856948853}],"awards":[{"id":"https://openalex.org/G2614585513","display_name":null,"funder_award_id":"2020YFC2200704","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4394715556.pdf"},"referenced_works_count":49,"referenced_works":["https://openalex.org/W1969509314","https://openalex.org/W2005806644","https://openalex.org/W2029172868","https://openalex.org/W2089441731","https://openalex.org/W2136922672","https://openalex.org/W2156515534","https://openalex.org/W2754288225","https://openalex.org/W2793623685","https://openalex.org/W2896344704","https://openalex.org/W2899186693","https://openalex.org/W2955879637","https://openalex.org/W2971058209","https://openalex.org/W2979645297","https://openalex.org/W2993817755","https://openalex.org/W2998812258","https://openalex.org/W2999762823","https://openalex.org/W3006265495","https://openalex.org/W3009585895","https://openalex.org/W3009936147","https://openalex.org/W3010049884","https://openalex.org/W3019166713","https://openalex.org/W3046520725","https://openalex.org/W3047238636","https://openalex.org/W3102052605","https://openalex.org/W3124596402","https://openalex.org/W3168220422","https://openalex.org/W3171641119","https://openalex.org/W3174413026","https://openalex.org/W3179242461","https://openalex.org/W3183542811","https://openalex.org/W3190174881","https://openalex.org/W3191891399","https://openalex.org/W4221083323","https://openalex.org/W4226316508","https://openalex.org/W4291805238","https://openalex.org/W4293732113","https://openalex.org/W4306769438","https://openalex.org/W4312545380","https://openalex.org/W4319829711","https://openalex.org/W4320496333","https://openalex.org/W4362714483","https://openalex.org/W4366386389","https://openalex.org/W4376629790","https://openalex.org/W4384158709","https://openalex.org/W4387796002","https://openalex.org/W6781221477","https://openalex.org/W6785659962","https://openalex.org/W6852192352","https://openalex.org/W6852420846"],"related_works":["https://openalex.org/W1185300216","https://openalex.org/W2954163146","https://openalex.org/W1996835655","https://openalex.org/W2896057011","https://openalex.org/W3188962172","https://openalex.org/W2772917594","https://openalex.org/W4312825515","https://openalex.org/W4238675884","https://openalex.org/W4306742369","https://openalex.org/W2589817099"],"abstract_inverted_index":{"Deep":[0],"learning":[1,55,118],"(DL)":[2],"has":[3],"been":[4],"frequently":[5],"applied":[6],"in":[7,15,123,128,183],"the":[8,20,37,77,80,87,92,105,111,116,129,132,151,173,179],"image":[9,25,166,180],"reconstruction":[10,81,181],"of":[11,91,107,131],"electromagnetic":[12,56],"tomography":[13,57],"(EMT)":[14],"recent":[16],"years.":[17],"It":[18],"offers":[19],"potential":[21],"to":[22,48,110,157],"achieve":[23],"higher-quality":[24],"reconstruction.":[26,167],"Among":[27],"these,":[28],"research":[29],"on":[30,73,150],"samples":[31,69,109],"is":[32,46,63,83,148,192],"relatively":[33],"scarce.":[34],"Samples":[35],"are":[36,70],"cornerstone":[38],"for":[39,141,164],"both":[40],"large":[41],"and":[42,67,89,186],"small":[43,185],"models,":[44],"which":[45],"easy":[47],"ignore.":[49],"In":[50],"this":[51,74],"paper,":[52],"a":[53,102,138,143,159,184],"deep":[54,117],"(DL-EMT)":[58],"model":[59],"with":[60],"nine":[61],"elements":[62],"established.":[64],"Complete":[65],"simulation":[66],"experimental":[68],"obtained":[71],"based":[72,149],"model.":[75],"On":[76],"sample":[78,144,162,188],"sets,":[79],"quality":[82,130],"observed":[84],"by":[85,195],"adjusting":[86],"size":[88],"configuration":[90],"training":[93,112],"set.":[94,145],"The":[95,168],"Mann-Whitney":[96],"U":[97],"test":[98],"shows":[99,171],"that":[100,172],"beyond":[101],"certain":[103],"point,":[104],"addition":[106],"more":[108,160],"data":[113],"fed":[114],"into":[115],"network":[119],"does":[120],"not":[121],"result":[122],"an":[124],"obvious":[125],"improvement":[126],"statistically":[127],"reconstructed":[133],"images.":[134],"This":[135,146,190],"paper":[136],"proposes":[137],"CC-building":[139,174],"method":[140,147,175,191],"optimizing":[142],"Pearson":[152],"correlation":[153],"coefficient":[154],"calculation,":[155],"aiming":[156],"establish":[158],"effective":[161],"base":[163],"DL-EMT":[165],"statistical":[169],"analysis":[170],"can":[176],"significantly":[177],"improve":[178],"effect":[182],"moderate":[187],"size.":[189],"also":[193],"validated":[194],"experiments.":[196]},"counts_by_year":[{"year":2025,"cited_by_count":5}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}