{"id":"https://openalex.org/W4214735217","doi":"https://doi.org/10.1145/3488933.3488997","title":"A Detection Method for Pneumonia Lesions Based on Multi-scale Dilated Convolution","display_name":"A Detection Method for Pneumonia Lesions Based on Multi-scale Dilated Convolution","publication_year":2021,"publication_date":"2021-09-24","ids":{"openalex":"https://openalex.org/W4214735217","doi":"https://doi.org/10.1145/3488933.3488997"},"language":"en","primary_location":{"id":"doi:10.1145/3488933.3488997","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3488933.3488997","pdf_url":null,"source":{"id":"https://openalex.org/S4363608564","display_name":"2021 4th International Conference on Artificial Intelligence and Pattern Recognition","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 4th International Conference on Artificial Intelligence and Pattern Recognition","raw_type":"proceedings-article"},"type":"article","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/A5066483084","display_name":"Yining Chen","orcid":"https://orcid.org/0000-0002-9501-5293"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yining Chen","raw_affiliation_strings":["Xi'an University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Xi'an University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045234445","display_name":"Yagang Wang","orcid":"https://orcid.org/0000-0003-3037-9798"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yagang Wang","raw_affiliation_strings":["Xi'an University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Xi'an University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5066483084"],"corresponding_institution_ids":["https://openalex.org/I4210136859"],"apc_list":null,"apc_paid":null,"fwci":0.3021,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.48632219,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"185","last_page":"190"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9994999766349792,"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"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9909999966621399,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9825000166893005,"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/kernel","display_name":"Kernel (algebra)","score":0.6235170364379883},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.568953812122345},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5659023523330688},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.515798807144165},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5126796960830688},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5053477883338928},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.4927347004413605},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.48433148860931396},{"id":"https://openalex.org/keywords/dilation","display_name":"Dilation (metric space)","score":0.45895251631736755},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3484179973602295},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2843073606491089},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.19619843363761902}],"concepts":[{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6235170364379883},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.568953812122345},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5659023523330688},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.515798807144165},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5126796960830688},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5053477883338928},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.4927347004413605},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.48433148860931396},{"id":"https://openalex.org/C2780757906","wikidata":"https://www.wikidata.org/wiki/Q5276676","display_name":"Dilation (metric space)","level":2,"score":0.45895251631736755},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3484179973602295},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2843073606491089},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.19619843363761902},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3488933.3488997","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3488933.3488997","pdf_url":null,"source":{"id":"https://openalex.org/S4363608564","display_name":"2021 4th International Conference on Artificial Intelligence and Pattern Recognition","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 4th International Conference on Artificial Intelligence and Pattern Recognition","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2194775991","https://openalex.org/W2565639579","https://openalex.org/W2792454083","https://openalex.org/W2888397986","https://openalex.org/W2956897601","https://openalex.org/W2963604034","https://openalex.org/W2964241181","https://openalex.org/W2972006294","https://openalex.org/W2974461651","https://openalex.org/W2980182400","https://openalex.org/W2988452521","https://openalex.org/W3013134080","https://openalex.org/W3029443186","https://openalex.org/W3080937139","https://openalex.org/W3106234331"],"related_works":["https://openalex.org/W2051113792","https://openalex.org/W4287548622","https://openalex.org/W3173347409","https://openalex.org/W3034421924","https://openalex.org/W2982536526","https://openalex.org/W4386858688","https://openalex.org/W4380302312","https://openalex.org/W3008689640","https://openalex.org/W4385338604","https://openalex.org/W3081626085"],"abstract_inverted_index":{"In":[0],"order":[1],"to":[2,115,144,202],"solve":[3,194],"the":[4,13,17,43,83,86,97,108,128,137,142,154,161,170,181,188,195,203],"problem":[5,196],"of":[6,20,45,52,63,68,85,122,124,133,141,147,164,197,206],"missed":[7],"diagnosis":[8],"and":[9,49,70,93,131,135,139,191],"misdiagnosis":[10],"caused":[11],"by":[12,160],"large":[14],"difference":[15],"in":[16,23,187],"size":[18,48,67],"distribution":[19],"pneumonia":[21,31,155,207],"lesions":[22,32,123],"chest":[24],"X-ray":[25],"films,":[26],"a":[27,58,64],"detection":[28,179,189,199],"method":[29],"for":[30,77],"combined":[33],"with":[34,89,177],"multi-scale":[35,78,98],"dilated":[36,53,60,87],"convolution":[37,46,54,61,65,88],"network":[38,105,143],"is":[39,75,102,113,174],"proposed.":[40],"By":[41,81],"analyzing":[42],"influence":[44],"kernel":[47,66],"dilation":[50,72],"rate":[51,73],"on":[55,153],"receptive":[56,91,110],"field,":[57,111],"hybrid":[59],"composed":[62],"3x3":[69],"different":[71,90,125,148,204],"parameters":[74],"used":[76],"feature":[79,100,120],"extraction.":[80],"stacking":[82],"branches":[84],"fields":[92],"adding":[94],"residual":[95],"structure,":[96],"fusion":[99],"representation":[101],"generated.":[103],"The":[104,150],"moderately":[106],"increases":[107],"effective":[109],"which":[112],"beneficial":[114],"obtain":[116],"more":[117],"abundant":[118],"local":[119],"information":[121],"sizes,":[126],"enhances":[127],"characterization":[129],"ability":[130],"robustness":[132],"features,":[134],"improves":[136],"adaptability":[138],"accuracy":[140,200],"lesion":[145],"targets":[146],"sizes.":[149],"experimental":[151],"results":[152],"public":[156],"data":[157],"set":[158],"provided":[159],"National":[162],"Institutes":[163],"Health":[165],"Clinical":[166],"Center":[167],"show":[168],"that":[169],"mean":[171],"average":[172],"precision":[173],"41.387%.":[175],"Compared":[176],"other":[178],"methods,":[180],"proposed":[182],"approach":[183],"has":[184],"obvious":[185],"improvement":[186],"accuracy,":[190],"can":[192],"effectively":[193],"low":[198],"due":[201],"sizes":[205],"lesions.":[208]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
