{"id":"https://openalex.org/W3120970682","doi":"https://doi.org/10.1145/3431943.3432286","title":"Multi-scale Hierarchy Feature Fusion Generative Adversarial Network for Low-Dose CT Denoising","display_name":"Multi-scale Hierarchy Feature Fusion Generative Adversarial Network for Low-Dose CT Denoising","publication_year":2020,"publication_date":"2020-10-16","ids":{"openalex":"https://openalex.org/W3120970682","doi":"https://doi.org/10.1145/3431943.3432286","mag":"3120970682"},"language":"en","primary_location":{"id":"doi:10.1145/3431943.3432286","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3431943.3432286","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 9th International Conference on Bioinformatics and Biomedical Science","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/A5101798826","display_name":"Ying Bai","orcid":"https://orcid.org/0000-0001-6907-9253"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ying Bai","raw_affiliation_strings":["School of Computer Science and Technology Anhui University"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology Anhui University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089182631","display_name":"Haifeng Zhao","orcid":"https://orcid.org/0000-0002-5196-4921"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haifeng Zhao","raw_affiliation_strings":["School of Computer Science and Technology Anhui University"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology Anhui University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100768910","display_name":"Shaojie Zhang","orcid":"https://orcid.org/0009-0007-9858-8939"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shaojie Zhang","raw_affiliation_strings":["School of Computer Science and Technology Anhui University"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology Anhui University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064564558","display_name":"Dong Nie","orcid":"https://orcid.org/0000-0003-0385-8988"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dong Nie","raw_affiliation_strings":["Department of Computer Science University of North Carolina at Chapel Hill"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science University of North Carolina at Chapel Hill","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101553435","display_name":"Zhenyu Tang","orcid":"https://orcid.org/0000-0002-6998-2669"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenyu Tang","raw_affiliation_strings":["Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University"],"affiliations":[{"raw_affiliation_string":"Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101798826"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0977,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.43432816,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"1","issue":null,"first_page":"102","last_page":"106"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9993000030517578,"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"}},"topics":[{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9993000030517578,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9973999857902527,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9969000220298767,"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/computer-science","display_name":"Computer science","score":0.7281306385993958},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6112004518508911},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.610071063041687},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5766935348510742},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5163039565086365},{"id":"https://openalex.org/keywords/hierarchy","display_name":"Hierarchy","score":0.5023140907287598},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5011749267578125},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.49799323081970215},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48431459069252014},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.46129927039146423},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4511309862136841},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42569732666015625},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3602296710014343},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1902683973312378},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.11514115333557129}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7281306385993958},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6112004518508911},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.610071063041687},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5766935348510742},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5163039565086365},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.5023140907287598},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5011749267578125},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.49799323081970215},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48431459069252014},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.46129927039146423},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4511309862136841},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42569732666015625},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3602296710014343},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1902683973312378},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.11514115333557129},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C34447519","wikidata":"https://www.wikidata.org/wiki/Q179522","display_name":"Market economy","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3431943.3432286","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3431943.3432286","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 9th International Conference on Bioinformatics and Biomedical Science","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":23,"referenced_works":["https://openalex.org/W54257720","https://openalex.org/W1901129140","https://openalex.org/W1949839429","https://openalex.org/W1964231221","https://openalex.org/W2002611249","https://openalex.org/W2041114617","https://openalex.org/W2044774090","https://openalex.org/W2047169513","https://openalex.org/W2133665775","https://openalex.org/W2143163922","https://openalex.org/W2160547390","https://openalex.org/W2748739903","https://openalex.org/W2766610223","https://openalex.org/W2787564202","https://openalex.org/W2789713147","https://openalex.org/W2798401174","https://openalex.org/W2946539594","https://openalex.org/W3103261259","https://openalex.org/W3106320098","https://openalex.org/W3203927719","https://openalex.org/W4244955044","https://openalex.org/W4296142362","https://openalex.org/W6891762490"],"related_works":["https://openalex.org/W2365264209","https://openalex.org/W2560215812","https://openalex.org/W2509431957","https://openalex.org/W2949601986","https://openalex.org/W2026999166","https://openalex.org/W2788972299","https://openalex.org/W4375867731","https://openalex.org/W4390516098","https://openalex.org/W2521347458","https://openalex.org/W1992685502"],"abstract_inverted_index":{"Image":[0],"noise":[1],"is":[2,81,112,123],"an":[3],"inherent":[4],"issue":[5],"in":[6,151],"low-dose":[7],"CT":[8],"(LDCT).":[9],"Increasing":[10],"radiation":[11],"dose":[12],"can":[13],"alleviate":[14],"this":[15,55],"problem":[16],"to":[17,26,83,126],"some":[18],"extent,":[19],"but":[20],"it":[21],"also":[22,124],"brings":[23],"potential":[24],"risks":[25],"the":[27,69,92,98,107,113,128,131,140,146],"patients.":[28],"Thus,":[29],"LDCT":[30,43,73],"denoising":[31,44],"has":[32],"raised":[33],"increasing":[34],"attention":[35],"from":[36],"researchers.":[37],"Currently,":[38],"many":[39],"deep":[40],"learning":[41,122],"based":[42,65],"methods":[45,148],"have":[46,138],"been":[47],"proposed":[48],"with":[49,87,91],"success,":[50],"such":[51],"as":[52],"encoder-decoder.":[53],"In":[54,119],"paper,":[56],"we":[57],"propose":[58],"a":[59,76],"novel":[60],"multi-scale":[61,78],"hierarchy":[62],"feature":[63],"fusion":[64],"encoder-decoder":[66],"network":[67],"within":[68],"GAN":[70],"framework":[71],"for":[72],"denoising.":[74],"Specifically,":[75],"four-stage":[77],"dilated":[79],"blocks":[80],"introduced":[82],"integrate":[84],"low-level":[85,102,117],"features":[86,103],"high-level":[88,105],"features.":[89],"Comparing":[90],"conventional":[93],"skip":[94],"connection,":[95],"which":[96],"ignores":[97],"semantic":[99],"gap":[100],"between":[101],"and":[104,155],"features,":[106],"advantage":[108],"of":[109,116,130,142],"our":[110,143],"method":[111,144],"effective":[114],"use":[115],"information.":[118],"addition,":[120],"residual":[121],"adopted":[125],"boost":[127],"training":[129],"network.":[132],"Experimental":[133],"results":[134],"on":[135],"public":[136],"dataset":[137],"demonstrated":[139],"superiority":[141],"over":[145],"state-of-the-art":[147],"under":[149],"comparison":[150],"both":[152],"visual":[153],"quality":[154],"quantitative":[156],"evaluation.":[157]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}