{"id":"https://openalex.org/W3194306298","doi":"https://doi.org/10.1145/3468920.3468930","title":"Bone Age Assessment Based on Deep Convolution Neural Network","display_name":"Bone Age Assessment Based on Deep Convolution Neural Network","publication_year":2021,"publication_date":"2021-05-29","ids":{"openalex":"https://openalex.org/W3194306298","doi":"https://doi.org/10.1145/3468920.3468930","mag":"3194306298"},"language":"en","primary_location":{"id":"doi:10.1145/3468920.3468930","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3468920.3468930","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2021 3rd International Conference on Big Data Engineering","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/A5029707367","display_name":"Shoujian Yu","orcid":"https://orcid.org/0000-0002-8092-7556"},"institutions":[{"id":"https://openalex.org/I181326427","display_name":"Donghua University","ror":"https://ror.org/035psfh38","country_code":"CN","type":"education","lineage":["https://openalex.org/I181326427"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shoujian Yu","raw_affiliation_strings":["Donghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Donghua University, China","institution_ids":["https://openalex.org/I181326427"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057707406","display_name":"Jianbang Ge","orcid":"https://orcid.org/0000-0003-3916-4195"},"institutions":[{"id":"https://openalex.org/I181326427","display_name":"Donghua University","ror":"https://ror.org/035psfh38","country_code":"CN","type":"education","lineage":["https://openalex.org/I181326427"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianbang Ge","raw_affiliation_strings":["Donghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Donghua University, China","institution_ids":["https://openalex.org/I181326427"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038600448","display_name":"Xiaolin Xia","orcid":"https://orcid.org/0000-0002-6617-5589"},"institutions":[{"id":"https://openalex.org/I181326427","display_name":"Donghua University","ror":"https://ror.org/035psfh38","country_code":"CN","type":"education","lineage":["https://openalex.org/I181326427"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolin Xia","raw_affiliation_strings":["Donghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Donghua University, China","institution_ids":["https://openalex.org/I181326427"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I181326427"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0990099,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"70","last_page":"75"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10992","display_name":"Forensic Anthropology and Bioarchaeology Studies","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1204","display_name":"Archeology"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10992","display_name":"Forensic Anthropology and Bioarchaeology Studies","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1204","display_name":"Archeology"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9878000020980835,"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/T11363","display_name":"Dental Radiography and Imaging","score":0.9768999814987183,"subfield":{"id":"https://openalex.org/subfields/3504","display_name":"Oral Surgery"},"field":{"id":"https://openalex.org/fields/35","display_name":"Dentistry"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7724237442016602},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7530880570411682},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6819243431091309},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6319217681884766},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5985837578773499},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5626384019851685},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5616816282272339},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.5519199967384338},{"id":"https://openalex.org/keywords/bone-age","display_name":"Bone age","score":0.5411493182182312},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5379986763000488},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45810389518737793},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.43513157963752747},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.41026079654693604},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34596383571624756},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33005964756011963},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.1132807731628418}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7724237442016602},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7530880570411682},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6819243431091309},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6319217681884766},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5985837578773499},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5626384019851685},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5616816282272339},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.5519199967384338},{"id":"https://openalex.org/C89551170","wikidata":"https://www.wikidata.org/wiki/Q1777274","display_name":"Bone age","level":2,"score":0.5411493182182312},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5379986763000488},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45810389518737793},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43513157963752747},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.41026079654693604},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34596383571624756},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33005964756011963},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.1132807731628418},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3468920.3468930","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3468920.3468930","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2021 3rd International Conference on Big Data Engineering","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":20,"referenced_works":["https://openalex.org/W1533861849","https://openalex.org/W1686810756","https://openalex.org/W2058333183","https://openalex.org/W2069821758","https://openalex.org/W2113834326","https://openalex.org/W2162206900","https://openalex.org/W2183341477","https://openalex.org/W2616247523","https://openalex.org/W2618245351","https://openalex.org/W2752782242","https://openalex.org/W2759532730","https://openalex.org/W2777464750","https://openalex.org/W2951934944","https://openalex.org/W2952975261","https://openalex.org/W2964121744","https://openalex.org/W2968043694","https://openalex.org/W3034328552","https://openalex.org/W3102564565","https://openalex.org/W3143250005","https://openalex.org/W3154811403"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W2957862417","https://openalex.org/W2897589971","https://openalex.org/W4309644067","https://openalex.org/W2068327088","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W2061761887","https://openalex.org/W4210566992","https://openalex.org/W2964954556"],"abstract_inverted_index":{"Bone":[0],"age":[1,21,125,141,160],"assessment":[2,22,126,161],"has":[3,66],"become":[4],"a":[5],"common":[6],"clinical":[7],"practice":[8],"in":[9,55,129],"the":[10,38,51,59,97,102,114,134,137,145,152,155,169,172,176],"diagnosis":[11],"of":[12,104,136,148,154],"endocrine":[13],"disorders":[14],"and":[15,41,65,73,87,94,121,131,175],"bone":[16,20,106,124,140,159,177],"maturity.":[17],"An":[18],"end-to-end":[19],"method":[23],"based":[24],"on":[25,83,133],"deep":[26,34],"convolutional":[27],"neural":[28],"networks":[29],"is":[30,61,117,162,165],"proposed,":[31],"which":[32,164],"uses":[33],"learning":[35],"to":[36,63,81,119,167],"segment":[37],"hand":[39,47,105],"region":[40,156],"predict":[42],"key":[43],"points":[44],"for":[45,57,158],"correcting":[46],"posture.":[48],"By":[49,89],"optimizing":[50],"dice":[52],"loss":[53],"function":[54],"DenseUNet":[56],"segmentation,":[58],"model":[60,99],"easier":[62],"converge":[64],"better":[67],"segmentation":[68],"quality.":[69],"Squeeze-and-excitation":[70],"(SE)":[71],"module":[72,77],"Position/Channel":[74],"Attention":[75],"(PA/CA)":[76],"can":[78,100],"help":[79],"network":[80,116],"focus":[82],"useful":[84],"feature":[85],"channels":[86],"position.":[88],"merging":[90],"InceptionV3":[91],"with":[92],"SE-module":[93],"PA/CA":[95],"module,":[96],"assesssment":[98],"extract":[101],"features":[103],"more":[107],"efficiently.":[108],"The":[109],"experimental":[110],"results":[111],"show":[112],"that":[113],"proposed":[115],"superior":[118],"traditional":[120],"other":[122],"automated":[123],"methods":[127],"both":[128],"accuracy":[130],"efficiency":[132],"data":[135],"2017":[138],"children's":[139],"challenge":[142],"organized":[143],"by":[144],"Radiological":[146],"Society":[147],"North":[149],"America.":[150],"Finally,":[151],"visualization":[153],"activation":[157],"implemented,":[163],"helpful":[166],"understand":[168],"connection":[170],"between":[171],"image":[173],"appearance":[174],"age.":[178]},"counts_by_year":[],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2025-10-10T00:00:00"}