{"id":"https://openalex.org/W7138414939","doi":"https://doi.org/10.48550/arxiv.2603.15025","title":"One CT Unified Model Training Framework to Rule All Scanning Protocols","display_name":"One CT Unified Model Training Framework to Rule All Scanning Protocols","publication_year":2026,"publication_date":"2026-03-16","ids":{"openalex":"https://openalex.org/W7138414939","doi":"https://doi.org/10.48550/arxiv.2603.15025"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.15025","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15025","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.15025","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129652192","display_name":"Fengzhi Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Fengzhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019886356","display_name":"Ziyuan Yang","orcid":"https://orcid.org/0000-0002-0275-4098"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Ziyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129670271","display_name":"Zexin Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Zexin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129716579","display_name":"Yingyu Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yingyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129730850","display_name":"Fenglei Fan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fan, Fenglei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049086157","display_name":"Hongming Shan","orcid":"https://orcid.org/0000-0002-0604-3197"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shan, Hongming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129751847","display_name":"Yi Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yi","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/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.5041999816894531,"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"}},"topics":[{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.5041999816894531,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.3723999857902527,"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/T10844","display_name":"Radiation Dose and Imaging","score":0.050999999046325684,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.6585999727249146},{"id":"https://openalex.org/keywords/unified-model","display_name":"Unified Model","score":0.605400025844574},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5501999855041504},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.5164999961853027},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47130000591278076},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.41200000047683716},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.40790000557899475}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7433000206947327},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.6585999727249146},{"id":"https://openalex.org/C45493050","wikidata":"https://www.wikidata.org/wiki/Q7884934","display_name":"Unified Model","level":2,"score":0.605400025844574},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5501999855041504},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.538100004196167},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.5164999961853027},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47130000591278076},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.41200000047683716},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.40790000557899475},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4034999907016754},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.37220001220703125},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3100000023841858},{"id":"https://openalex.org/C71559656","wikidata":"https://www.wikidata.org/wiki/Q671298","display_name":"Divide and conquer algorithms","level":2,"score":0.30149999260902405},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.3012000024318695},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29269999265670776},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2624000012874603},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2565999925136566}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.15025","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15025","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.15025","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15025","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":"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":{"Non-ideal":[0],"measurement":[1],"computed":[2],"tomography":[3],"(NICT),":[4],"which":[5,35,79,127,196],"lowers":[6],"radiation":[7],"at":[8],"the":[9,16,59,90,113,129,135,140,146,161,164,179,194,207,230],"cost":[10],"of":[11,19,55,61,131,163,232],"image":[12],"quality,":[13],"is":[14,36],"expanding":[15],"clinical":[17],"use":[18],"CT.":[20],"Although":[21],"unified":[22],"models":[23],"have":[24],"shown":[25],"promise":[26],"in":[27,89,119],"NICT":[28],"enhancement,":[29],"most":[30],"methods":[31],"require":[32],"paired":[33],"data,":[34],"an":[37,105],"impractical":[38],"demand":[39],"due":[40],"to":[41,48,65,81,111,160,169,176,200,210,228],"inevitable":[42],"organ":[43],"motion.":[44],"Unsupervised":[45],"approaches":[46],"attempt":[47],"overcome":[49],"this":[50],"limitation,":[51],"but":[52],"their":[53,98],"assumption":[54],"homogeneous":[56],"noise":[57],"neglects":[58],"variability":[60],"scanning":[62,77],"protocols,":[63,78],"leading":[64],"poor":[66],"generalization":[67],"and":[68,96,123,151,155,181,189,214],"potential":[69],"model":[70,171],"collapse.":[71],"We":[72],"further":[73],"observe":[74],"that":[75],"distinct":[76],"correspond":[80],"different":[82,236],"physical":[83],"imaging":[84],"processes,":[85],"produce":[86],"discrete":[87,149],"sub-manifolds":[88,122],"feature":[91,157],"space,":[92],"contradicting":[93],"these":[94],"assumptions":[95],"limiting":[97],"effectiveness.":[99],"To":[100],"address":[101],"this,":[102],"we":[103,174,185],"propose":[104,175],"Uncertainty-Guided":[106],"Manifold":[107],"Smoothing":[108],"(UMS)":[109],"framework":[110],"bridge":[112],"gaps":[114,147],"between":[115,148],"sub-manifolds.":[116],"A":[117],"classifier":[118],"UMS":[120,143],"identifies":[121],"predicts":[124],"uncertainty":[125],"scores,":[126],"guide":[128],"generation":[130,237],"diverse":[132],"samples":[133],"across":[134,235],"entire":[136],"manifold.":[137],"By":[138],"leveraging":[139],"classifier's":[141],"capability,":[142],"effectively":[144],"fills":[145],"sub-manifolds,":[150],"promotes":[152],"a":[153,187],"continuous":[154],"dense":[156],"space.":[158],"Due":[159],"complexity":[162],"global":[165],"manifold,":[166],"it's":[167],"hard":[168],"directly":[170],"it.":[172],"Therefore,":[173],"dynamically":[177],"incorporate":[178],"global-":[180,188],"sub-manifold-specific":[182],"features.":[183],"Specifically,":[184],"design":[186],"sub-manifold-driven":[190],"architecture":[191],"guided":[192],"by":[193],"classifier,":[195],"enables":[197],"dynamic":[198,204],"adaptation":[199],"subdomain":[201],"variations.":[202],"This":[203],"mechanism":[205],"improves":[206],"network's":[208],"capacity":[209],"capture":[211],"both":[212],"shared":[213],"domain-specific":[215],"features,":[216],"thereby":[217],"improving":[218],"reconstruction":[219],"performance.":[220],"Extensive":[221],"experiments":[222],"on":[223],"public":[224],"datasets":[225],"are":[226],"conducted":[227],"validate":[229],"effectiveness":[231],"our":[233],"method":[234],"paradigms.":[238]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-18T00:00:00"}