{"id":"https://openalex.org/W4313527287","doi":"https://doi.org/10.1109/bibm55620.2022.9995643","title":"MFA-DSLNet: Multi-scale Fusion Attention Mechanism with Dual-threshold Self-paced Learning for Pediatric Pneumonia Diagnosis","display_name":"MFA-DSLNet: Multi-scale Fusion Attention Mechanism with Dual-threshold Self-paced Learning for Pediatric Pneumonia Diagnosis","publication_year":2022,"publication_date":"2022-12-06","ids":{"openalex":"https://openalex.org/W4313527287","doi":"https://doi.org/10.1109/bibm55620.2022.9995643"},"language":"en","primary_location":{"id":"doi:10.1109/bibm55620.2022.9995643","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bibm55620.2022.9995643","pdf_url":null,"source":{"id":"https://openalex.org/S4363607730","display_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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/A5101602236","display_name":"Yibo Feng","orcid":"https://orcid.org/0000-0002-5589-3323"},"institutions":[{"id":"https://openalex.org/I4210111607","display_name":"InferVision (China)","ror":"https://ror.org/027h3dg90","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210111607"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yibo Feng","raw_affiliation_strings":["Beijing Infervision Technology CO., Ltd.,Beijing,China","Beijing Infervision Technology CO., Ltd., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Infervision Technology CO., Ltd.,Beijing,China","institution_ids":["https://openalex.org/I4210111607"]},{"raw_affiliation_string":"Beijing Infervision Technology CO., Ltd., Beijing, China","institution_ids":["https://openalex.org/I4210111607"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107306837","display_name":"Yu Wang","orcid":"https://orcid.org/0000-0001-8697-2940"},"institutions":[{"id":"https://openalex.org/I4210111607","display_name":"InferVision (China)","ror":"https://ror.org/027h3dg90","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210111607"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yudong Wang","raw_affiliation_strings":["Beijing Infervision Technology CO., Ltd.,Beijing,China","Beijing Infervision Technology CO., Ltd., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Infervision Technology CO., Ltd.,Beijing,China","institution_ids":["https://openalex.org/I4210111607"]},{"raw_affiliation_string":"Beijing Infervision Technology CO., Ltd., Beijing, China","institution_ids":["https://openalex.org/I4210111607"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070530976","display_name":"Ruize Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210111607","display_name":"InferVision (China)","ror":"https://ror.org/027h3dg90","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210111607"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruize Yu","raw_affiliation_strings":["Beijing Infervision Technology CO., Ltd.,Beijing,China","Beijing Infervision Technology CO., Ltd., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Infervision Technology CO., Ltd.,Beijing,China","institution_ids":["https://openalex.org/I4210111607"]},{"raw_affiliation_string":"Beijing Infervision Technology CO., Ltd., Beijing, China","institution_ids":["https://openalex.org/I4210111607"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072622467","display_name":"Yiqing Shen","orcid":"https://orcid.org/0000-0001-7866-3339"},"institutions":[{"id":"https://openalex.org/I4210111607","display_name":"InferVision (China)","ror":"https://ror.org/027h3dg90","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210111607"]},{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Yiqing Shen","raw_affiliation_strings":["Beijing Infervision Technology CO., Ltd.,Beijing,China","Johns Hopkins University, MD, USA","Beijing Infervision Technology CO., Ltd., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Infervision Technology CO., Ltd.,Beijing,China","institution_ids":["https://openalex.org/I4210111607"]},{"raw_affiliation_string":"Johns Hopkins University, MD, USA","institution_ids":["https://openalex.org/I145311948"]},{"raw_affiliation_string":"Beijing Infervision Technology CO., Ltd., Beijing, China","institution_ids":["https://openalex.org/I4210111607"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101602342","display_name":"Shaokang Wang","orcid":"https://orcid.org/0000-0003-2691-0013"},"institutions":[{"id":"https://openalex.org/I4210111607","display_name":"InferVision (China)","ror":"https://ror.org/027h3dg90","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210111607"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaokang Wang","raw_affiliation_strings":["Beijing Infervision Technology CO., Ltd.,Beijing,China","Beijing Infervision Technology CO., Ltd., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Infervision Technology CO., Ltd.,Beijing,China","institution_ids":["https://openalex.org/I4210111607"]},{"raw_affiliation_string":"Beijing Infervision Technology CO., Ltd., Beijing, China","institution_ids":["https://openalex.org/I4210111607"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063317105","display_name":"Jiangfen Wu","orcid":"https://orcid.org/0000-0001-7353-997X"},"institutions":[{"id":"https://openalex.org/I4210111607","display_name":"InferVision (China)","ror":"https://ror.org/027h3dg90","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210111607"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiangfen Wu","raw_affiliation_strings":["Beijing Infervision Technology CO., Ltd.,Beijing,China","Beijing Infervision Technology CO., Ltd., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Infervision Technology CO., Ltd.,Beijing,China","institution_ids":["https://openalex.org/I4210111607"]},{"raw_affiliation_string":"Beijing Infervision Technology CO., Ltd., Beijing, China","institution_ids":["https://openalex.org/I4210111607"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005606057","display_name":"Weidao Chen","orcid":"https://orcid.org/0000-0001-7730-916X"},"institutions":[{"id":"https://openalex.org/I4210111607","display_name":"InferVision (China)","ror":"https://ror.org/027h3dg90","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210111607"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weidao Chen","raw_affiliation_strings":["Beijing Infervision Technology CO., Ltd.,Beijing,China","Beijing Infervision Technology CO., Ltd., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Infervision Technology CO., Ltd.,Beijing,China","institution_ids":["https://openalex.org/I4210111607"]},{"raw_affiliation_string":"Beijing Infervision Technology CO., Ltd., Beijing, China","institution_ids":["https://openalex.org/I4210111607"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5101602236"],"corresponding_institution_ids":["https://openalex.org/I4210111607"],"apc_list":null,"apc_paid":null,"fwci":0.2235,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.5168388,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"9","issue":null,"first_page":"1530","last_page":"1533"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9998000264167786,"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":0.9998000264167786,"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/T10654","display_name":"Pneumonia and Respiratory Infections","score":0.9902999997138977,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"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/T11243","display_name":"Respiratory viral infections research","score":0.9776999950408936,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"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/computer-science","display_name":"Computer science","score":0.6910445094108582},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6433649659156799},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5890645980834961},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5453027486801147},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5347781181335449},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4851144552230835},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41860872507095337},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.41846275329589844},{"id":"https://openalex.org/keywords/pneumonia","display_name":"Pneumonia","score":0.4162253439426422},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3581022620201111},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.12244203686714172}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6910445094108582},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6433649659156799},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5890645980834961},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5453027486801147},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5347781181335449},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4851144552230835},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41860872507095337},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.41846275329589844},{"id":"https://openalex.org/C2777914695","wikidata":"https://www.wikidata.org/wiki/Q12192","display_name":"Pneumonia","level":2,"score":0.4162253439426422},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3581022620201111},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.12244203686714172},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm55620.2022.9995643","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bibm55620.2022.9995643","pdf_url":null,"source":{"id":"https://openalex.org/S4363607730","display_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2132984949","https://openalex.org/W2194775991","https://openalex.org/W2752782242","https://openalex.org/W2788633781","https://openalex.org/W2901989937","https://openalex.org/W2963091558","https://openalex.org/W2979643984","https://openalex.org/W2981899554","https://openalex.org/W2982220924","https://openalex.org/W3006841715","https://openalex.org/W3103326390","https://openalex.org/W3211983116","https://openalex.org/W4205623122","https://openalex.org/W4210390897","https://openalex.org/W4210913238","https://openalex.org/W4286377440","https://openalex.org/W6679390333","https://openalex.org/W6743731764"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W1495042958","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Chest":[0],"X-ray":[1],"(CXR)":[2],"is":[3,57,149],"crucial":[4],"to":[5,71,97,118,130,151,170],"the":[6,18,32,40,43,99,124,132,140,154,171,175],"early":[7],"diagnosis":[8],"and":[9,20,37,52,74,94,112,126],"treatment":[10],"of":[11,142,157],"pneumonia":[12,22],"in":[13,77,101,135],"children":[14],"while":[15],"still":[16],"facing":[17],"viral":[19,36,73],"bacterial":[21,38,75],"identification":[23,141],"challenge.":[24],"Although":[25],"convolutional":[26],"neural":[27],"networks":[28],"(CNNs)":[29],"can":[30],"identify":[31],"concealed":[33],"features":[34,96],"between":[35],"pneumonia,":[39],"influence":[41],"on":[42,174],"network":[44],"accuracy":[45],"led":[46],"by":[47],"morphological":[48],"lesion":[49],"size":[50],"variation":[51],"confounded":[53],"visual":[54],"imaging":[55],"findings":[56],"not":[58],"explicitly":[59],"addressed.":[60],"To":[61,138],"address":[62],"these":[63],"issues,":[64],"we":[65],"propose":[66],"a":[67,86,162,167],"novel":[68],"framework":[69],"MFA-DSLNet":[70,160],"classify":[72],"CXR":[76],"childhood":[78],"pneumonia.":[79],"It":[80],"comprises":[81],"two":[82,105],"functional":[83],"blocks.":[84],"First,":[85],"Multiscale":[87],"Fusion":[88],"Attention":[89,110,115],"(MFA)":[90],"mechanism":[91],"fuses":[92],"low-level":[93],"high-level":[95],"enrich":[98],"information":[100,122],"feature":[102,136],"maps.":[103,137],"Then,":[104],"attention":[106],"modules,":[107],"namely":[108],"Context":[109],"(CA)":[111],"Spatial":[113],"Position":[114],"(SPA),":[116],"attempt":[117],"extract":[119],"effective":[120],"latent":[121],"from":[123],"channel":[125],"spatial":[127],"dimension":[128],"respectively":[129],"tackle":[131],"redundancy":[133],"problem":[134],"facilitate":[139],"hard":[143],"cases,":[144],"Dual-threshold":[145],"Self-paced":[146],"Learning":[147],"(DSL)":[148],"proposed":[150],"dynamically":[152],"adapt":[153],"training":[155],"sequence":[156],"samples.":[158],"Empirically,":[159],"achieves":[161],"state-of-the-art":[163],"93.33%":[164],"accuracy,":[165],"with":[166],"2.33%":[168],"improvement":[169],"previous":[172],"works":[173],"ChestXray2017":[176],"dataset.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}