{"id":"https://openalex.org/W7160150285","doi":"https://doi.org/10.48550/arxiv.2605.00793","title":"Unsupervised Denoising of Real Clinical Low Dose Liver CT with Perceptual Attention Networks","display_name":"Unsupervised Denoising of Real Clinical Low Dose Liver CT with Perceptual Attention Networks","publication_year":2026,"publication_date":"2026-05-01","ids":{"openalex":"https://openalex.org/W7160150285","doi":"https://doi.org/10.48550/arxiv.2605.00793"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.00793","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00793","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.00793","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135267510","display_name":"Jingxi Pu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guan, Zhilin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5124279332","display_name":"Tonghua Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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":0.25929999351501465,"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":0.25929999351501465,"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/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.18080000579357147,"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/T11361","display_name":"Digital Radiography and Breast Imaging","score":0.06589999794960022,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6571000218391418},{"id":"https://openalex.org/keywords/computed-tomography","display_name":"Computed tomography","score":0.5573999881744385},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5460000038146973},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5077000260353088},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.4632999897003174},{"id":"https://openalex.org/keywords/clinical-practice","display_name":"Clinical Practice","score":0.4334000051021576},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.41690000891685486},{"id":"https://openalex.org/keywords/medical-diagnosis","display_name":"Medical diagnosis","score":0.4120999872684479}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7404000163078308},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6577000021934509},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6571000218391418},{"id":"https://openalex.org/C544519230","wikidata":"https://www.wikidata.org/wiki/Q32566","display_name":"Computed tomography","level":2,"score":0.5573999881744385},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5460000038146973},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5077000260353088},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4632999897003174},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.4334000051021576},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.41690000891685486},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.4120999872684479},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.3919000029563904},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35679998993873596},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.33169999718666077},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3208000063896179},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31709998846054077},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.3131999969482422},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.30410000681877136},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.30250000953674316},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2921999990940094},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26499998569488525},{"id":"https://openalex.org/C163716698","wikidata":"https://www.wikidata.org/wiki/Q841267","display_name":"Tomography","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.00793","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00793","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.00793","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00793","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,21,109,112,137,153,163],"development":[2],"of":[3,24,114,132,156],"deep":[4,29],"learning,":[5,66,175],"medical":[6,115,146],"image":[7],"processing":[8],"has":[9],"been":[10],"widely":[11],"used":[12,172],"to":[13,38,107,135],"assist":[14],"clinical":[15,167,193],"research.":[16],"This":[17],"paper":[18,68,158],"focuses":[19],"on":[20],"denoising":[22,76],"problem":[23],"low-dose":[25,32,73,123],"computed":[26,33,74,124],"tomography":[27,34,75,125],"using":[28,140],"learning.":[30],"Although":[31],"reduces":[35],"radiation":[36],"exposure":[37],"patients,":[39],"it":[40,161],"also":[41,103,185],"introduces":[42,104],"more":[43],"noise,":[44],"which":[45],"may":[46],"interfere":[47],"with":[48,150],"visual":[49],"interpretation":[50],"by":[51,62,188],"physicians":[52,190],"and":[53,95,127,145,191],"affect":[54],"diagnostic":[55],"results.":[56],"To":[57],"address":[58],"this":[59,67,157],"problem,":[60],"inspired":[61],"Cycle-GAN":[63],"for":[64,85,92,99,111,173],"unsupervised":[65,72],"proposes":[69],"an":[70,89],"end-to-end":[71],"framework.":[77],"The":[78,181],"proposed":[79,138],"framework":[80],"combines":[81],"a":[82,96,121,129],"U-Net":[83],"structure":[84],"multi-scale":[86],"feature":[87,93,100],"extraction,":[88],"attention":[90],"mechanism":[91],"fusion,":[94],"residual":[97],"network":[98,110],"transformation.":[101],"It":[102],"perceptual":[105],"loss":[106],"improve":[108],"characteristics":[113],"images.":[116],"In":[117],"addition,":[118],"we":[119],"construct":[120],"real":[122,166],"dataset":[126],"design":[128],"large":[130],"number":[131],"comparative":[133],"experiments":[134],"validate":[136],"method,":[139],"both":[141],"image-based":[142],"evaluation":[143,147],"metrics":[144],"criteria.":[148],"Compared":[149],"classical":[151],"methods,":[152],"main":[154],"advantage":[155],"is":[159],"that":[160,165],"addresses":[162],"limitation":[164],"data":[168],"cannot":[169],"be":[170],"directly":[171],"supervised":[174],"while":[176],"still":[177],"achieving":[178],"excellent":[179],"performance.":[180],"experimental":[182],"results":[183],"are":[184],"professionally":[186],"evaluated":[187],"imaging":[189],"meet":[192],"needs.":[194]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-05T00:00:00"}
