{"id":"https://openalex.org/W4388426591","doi":"https://doi.org/10.1109/bats59463.2023.10303265","title":"Blind Approach Using Convolutional Neural Networks to a New Ultrasound Image Denoising Task","display_name":"Blind Approach Using Convolutional Neural Networks to a New Ultrasound Image Denoising Task","publication_year":2023,"publication_date":"2023-09-28","ids":{"openalex":"https://openalex.org/W4388426591","doi":"https://doi.org/10.1109/bats59463.2023.10303265"},"language":"en","primary_location":{"id":"doi:10.1109/bats59463.2023.10303265","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bats59463.2023.10303265","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Workshop on Biomedical Applications, Technologies and Sensors (BATS)","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/A5004209529","display_name":"Miriam Basile","orcid":"https://orcid.org/0009-0004-5621-9834"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Miriam Basile","raw_affiliation_strings":["Università di Pisa,Dipartimento di Ingegneria dell'Informazione,Pisa,Italia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Università di Pisa,Dipartimento di Ingegneria dell'Informazione,Pisa,Italia","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020565251","display_name":"Fabio Gibiino","orcid":null},"institutions":[{"id":"https://openalex.org/I4210130470","display_name":"Esaote (Italy)","ror":"https://ror.org/02vmgn461","country_code":"IT","type":"company","lineage":["https://openalex.org/I4210130470"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Fabio Gibiino","raw_affiliation_strings":["Esaote S. p. A.,Genova,Italia","Esaote S. p. A., Genova, Italia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Esaote S. p. A.,Genova,Italia","institution_ids":["https://openalex.org/I4210130470"]},{"raw_affiliation_string":"Esaote S. p. A., Genova, Italia","institution_ids":["https://openalex.org/I4210130470"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043110801","display_name":"Jacopo Cavazza","orcid":"https://orcid.org/0000-0002-4912-6961"},"institutions":[{"id":"https://openalex.org/I4210130470","display_name":"Esaote (Italy)","ror":"https://ror.org/02vmgn461","country_code":"IT","type":"company","lineage":["https://openalex.org/I4210130470"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Jacopo Cavazza","raw_affiliation_strings":["Esaote S. p. A.,Genova,Italia","Esaote S. p. A., Genova, Italia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Esaote S. p. A.,Genova,Italia","institution_ids":["https://openalex.org/I4210130470"]},{"raw_affiliation_string":"Esaote S. p. A., Genova, Italia","institution_ids":["https://openalex.org/I4210130470"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002861507","display_name":"Paolo Semplici","orcid":null},"institutions":[{"id":"https://openalex.org/I4210130470","display_name":"Esaote (Italy)","ror":"https://ror.org/02vmgn461","country_code":"IT","type":"company","lineage":["https://openalex.org/I4210130470"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Paolo Semplici","raw_affiliation_strings":["Esaote S. p. A.,Genova,Italia","Esaote S. p. A., Genova, Italia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Esaote S. p. A.,Genova,Italia","institution_ids":["https://openalex.org/I4210130470"]},{"raw_affiliation_string":"Esaote S. p. A., Genova, Italia","institution_ids":["https://openalex.org/I4210130470"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007774813","display_name":"Alessio Bechini","orcid":"https://orcid.org/0000-0002-5951-1265"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alessio Bechini","raw_affiliation_strings":["Università di Pisa,Dipartimento di Ingegneria dell'Informazione,Pisa,Italia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Università di Pisa,Dipartimento di Ingegneria dell'Informazione,Pisa,Italia","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066412301","display_name":"Nicola Vanello","orcid":"https://orcid.org/0000-0002-2312-6699"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nicola Vanello","raw_affiliation_strings":["Università di Pisa,Dipartimento di Ingegneria dell'Informazione,Pisa,Italia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Università di Pisa,Dipartimento di Ingegneria dell'Informazione,Pisa,Italia","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2246,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.52696452,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9998000264167786,"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":0.9998000264167786,"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/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T12537","display_name":"Flow Measurement and Analysis","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/speckle-noise","display_name":"Speckle noise","score":0.8284434080123901},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7596928477287292},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7093387246131897},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6934790015220642},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6349647045135498},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5880845189094543},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.5655394196510315},{"id":"https://openalex.org/keywords/speckle-pattern","display_name":"Speckle pattern","score":0.5525203943252563},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.491941899061203},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43967756628990173},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.4357413649559021},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4101281464099884},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.34528830647468567},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08761012554168701}],"concepts":[{"id":"https://openalex.org/C180940675","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle noise","level":3,"score":0.8284434080123901},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7596928477287292},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7093387246131897},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6934790015220642},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6349647045135498},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5880845189094543},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.5655394196510315},{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.5525203943252563},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.491941899061203},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43967756628990173},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.4357413649559021},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4101281464099884},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.34528830647468567},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08761012554168701},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bats59463.2023.10303265","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bats59463.2023.10303265","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Workshop on Biomedical Applications, Technologies and Sensors (BATS)","raw_type":"proceedings-article"},{"id":"pmh:oai:arpi.unipi.it:11568/1216712","is_oa":false,"landing_page_url":"https://hdl.handle.net/11568/1216712","pdf_url":null,"source":{"id":"https://openalex.org/S4377196265","display_name":"CINECA IRIS Institutial research information system (University of Pisa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I108290504","host_organization_name":"University of Pisa","host_organization_lineage":["https://openalex.org/I108290504"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1985818917","https://openalex.org/W2011286292","https://openalex.org/W2028198328","https://openalex.org/W2028493053","https://openalex.org/W2046344557","https://openalex.org/W2061126127","https://openalex.org/W2072843185","https://openalex.org/W2093174557","https://openalex.org/W2096413230","https://openalex.org/W2109812093","https://openalex.org/W2112212587","https://openalex.org/W2112280212","https://openalex.org/W2122942014","https://openalex.org/W2127081471","https://openalex.org/W2130094715","https://openalex.org/W2130622335","https://openalex.org/W2140588814","https://openalex.org/W2140942156","https://openalex.org/W2147982258","https://openalex.org/W2171857449","https://openalex.org/W2281482349","https://openalex.org/W2763614502","https://openalex.org/W2793146153","https://openalex.org/W2929055273","https://openalex.org/W3017045385","https://openalex.org/W3037582100","https://openalex.org/W3169893408","https://openalex.org/W3173445401","https://openalex.org/W4237586647","https://openalex.org/W4242059867"],"related_works":["https://openalex.org/W2799624451","https://openalex.org/W2133290590","https://openalex.org/W2055824452","https://openalex.org/W2042914788","https://openalex.org/W2182190754","https://openalex.org/W4321264664","https://openalex.org/W2009383287","https://openalex.org/W2121688719","https://openalex.org/W2016481886","https://openalex.org/W1989852278"],"abstract_inverted_index":{"Speckle":[0],"noise":[1,17,27,64,78],"reduction":[2],"from":[3,65],"ultrasound":[4,66,81,97],"images":[5,74,93],"still":[6],"represents":[7],"a":[8],"challenge.":[9],"One":[10],"limiting":[11],"factor":[12],"is":[13,28],"the":[14,22,32],"difficulty":[15],"in":[16,104],"characterization":[18],"and":[19,94],"modeling.":[20],"Specifically,":[21],"removal":[23],"of":[24,34,106],"this":[25,52,107],"image-correlated":[26],"often":[29],"obtained":[30,75],"at":[31],"expense":[33],"signal":[35],"degradation.":[36],"Convolutional":[37],"neural":[38,58],"networks":[39,85],"have":[40,86],"been":[41,87],"shown":[42],"to":[43,61,79],"reach":[44],"good":[45],"performance":[46],"along":[47],"with":[48],"generalizability":[49],"properties.":[50],"In":[51],"work,":[53],"we":[54],"tested":[55,88],"an":[56],"unsupervised":[57],"network,":[59],"Noise2Noise,":[60],"reduce":[62],"speckle":[63],"images.":[67,82],"The":[68,83],"network":[69],"training":[70],"used":[71],"only":[72],"noisy":[73,92],"adding":[76],"synthetic":[77,91],"real":[80,96],"trained":[84],"both":[89],"on":[90,95],"acquisitions,":[98],"showing":[99],"promising":[100],"results":[101],"that":[102],"speak":[103],"favor":[105],"novel":[108],"denoising":[109],"approach.":[110]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2023-11-07T00:00:00"}