{"id":"https://openalex.org/W7138133962","doi":"https://doi.org/10.48550/arxiv.2603.13422","title":"Projection Guided Personalized Federated Learning for Low Dose CT Denoising","display_name":"Projection Guided Personalized Federated Learning for Low Dose CT Denoising","publication_year":2026,"publication_date":"2026-03-12","ids":{"openalex":"https://openalex.org/W7138133962","doi":"https://doi.org/10.48550/arxiv.2603.13422"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.13422","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13422","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.13422","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129690528","display_name":"Anas Zafar","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zafar, Anas","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129648070","display_name":"Muhammad Waqas","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Waqas, Muhammad","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038354395","display_name":"Amgad Muneer","orcid":"https://orcid.org/0000-0002-7157-3020"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Muneer, Amgad","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112864936","display_name":"Rukhmini Bandyopadhyay","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bandyopadhyay, Rukhmini","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129749238","display_name":"Jia Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Jia","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5129690528"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.43810001015663147,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.43810001015663147,"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/T11361","display_name":"Digital Radiography and Breast Imaging","score":0.0966000035405159,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.09220000356435776,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.718999981880188},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6141999959945679},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5848000049591064},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5669999718666077},{"id":"https://openalex.org/keywords/scanner","display_name":"Scanner","score":0.5148000121116638},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.47609999775886536},{"id":"https://openalex.org/keywords/protocol","display_name":"Protocol (science)","score":0.46000000834465027},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.3797000050544739}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7560999989509583},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.718999981880188},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6141999959945679},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.597100019454956},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5848000049591064},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5669999718666077},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5651000142097473},{"id":"https://openalex.org/C2779751349","wikidata":"https://www.wikidata.org/wiki/Q1474480","display_name":"Scanner","level":2,"score":0.5148000121116638},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.47609999775886536},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.46000000834465027},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.3797000050544739},{"id":"https://openalex.org/C544519230","wikidata":"https://www.wikidata.org/wiki/Q32566","display_name":"Computed tomography","level":2,"score":0.37560001015663147},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3621000051498413},{"id":"https://openalex.org/C2983327147","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Image denoising","level":3,"score":0.3522000014781952},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.35120001435279846},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.34769999980926514},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.2881999909877777},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C2987700449","wikidata":"https://www.wikidata.org/wiki/Q186161","display_name":"Radiation dose","level":2,"score":0.25220000743865967}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.13422","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13422","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.13422","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13422","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Low-dose":[0],"CT":[1,70,88,103],"(LDCT)":[2],"reduces":[3],"radiation":[4],"exposure":[5],"but":[6],"introduces":[7],"protocol-dependent":[8],"noise":[9,38,67],"and":[10,76,83,92,105,134],"artifacts":[11],"that":[12,52,86,99,110,125],"vary":[13],"across":[14],"institutions.":[15],"While":[16],"federated":[17,141],"learning":[18,142],"enables":[19],"collaborative":[20],"training":[21],"without":[22],"centralizing":[23],"patient":[24,40,91],"data,":[25],"existing":[26],"methods":[27],"personalize":[28,87],"in":[29,62],"image":[30,55],"space,":[31,65],"making":[32],"it":[33],"difficult":[34],"to":[35,90],"separate":[36],"scanner":[37],"from":[39],"anatomy.":[41],"We":[42],"propose":[43],"ProFed":[44,79,126],"(Projection":[45],"Guided":[46],"Personalized":[47],"Federated":[48],"Learning),":[49],"a":[50],"framework":[51],"complements":[53],"the":[54,63,119,145],"space":[56],"approach":[57],"by":[58,113,148],"performing":[59],"dual-level":[60],"personalization":[61],"projection":[64,97],"where":[66],"originates":[68],"during":[69],"measurements":[71],"before":[72],"reconstruction":[73,89],"combines":[74],"protocol":[75],"anatomy":[77],"effects.":[78],"introduces:":[80],"(i)":[81],"anatomy-aware":[82],"protocol-aware":[84],"networks":[85],"scanner-specific":[93],"features,":[94],"(ii)":[95],"multi-constraint":[96],"losses":[98],"enforce":[100],"consistency":[101],"with":[102,131,137],"measurements,":[104],"(iii)":[106],"uncertainty-guided":[107],"selective":[108],"aggregation":[109],"weights":[111],"clients":[112],"prediction":[114],"confidence.":[115],"Extensive":[116],"experiments":[117],"on":[118],"Mayo":[120],"Clinic":[121],"2016":[122],"dataset":[123],"demonstrate":[124],"achieves":[127],"42.56":[128],"dB":[129,136],"PSNR":[130],"CNN":[132],"backbones":[133],"44.83":[135],"Transformers,":[138],"outperforming":[139],"11":[140],"baselines,":[143],"including":[144],"physics-informed":[146],"SCAN-PhysFed":[147],"+1.42":[149],"dB.":[150]},"counts_by_year":[],"updated_date":"2026-03-18T06:31:55.123368","created_date":"2026-03-18T00:00:00"}