{"id":"https://openalex.org/W7164189818","doi":"https://doi.org/10.48550/arxiv.2606.10372","title":"ClinReadNet: A clinical reading-inspired network for low-dose abdominal CT image quality assessment","display_name":"ClinReadNet: A clinical reading-inspired network for low-dose abdominal CT image quality assessment","publication_year":2026,"publication_date":"2026-06-09","ids":{"openalex":"https://openalex.org/W7164189818","doi":"https://doi.org/10.48550/arxiv.2606.10372"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.10372","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10372","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.2606.10372","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121638724","display_name":"Xianye Xiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Xianye","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101164812","display_name":"Yulong Zou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zou, Yulong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017308147","display_name":"Yujie Luo","orcid":"https://orcid.org/0000-0003-1006-4342"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Yujie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030974005","display_name":"Taihui Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Taihui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121276086","display_name":"Cun-Jing Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Cun-Jing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121261914","display_name":"Yuan-ming Geng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Geng, Yuan-ming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138379502","display_name":"Shuihua Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Shuihua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121635573","display_name":"Yudong Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yudong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5121653721","display_name":"Jin Hong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hong, Jin","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/T11165","display_name":"Image and Video Quality Assessment","score":0.1850000023841858,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.1850000023841858,"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/T11361","display_name":"Digital Radiography and Breast Imaging","score":0.11710000038146973,"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/T11894","display_name":"Radiology practices and education","score":0.11649999767541885,"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/sobel-operator","display_name":"Sobel operator","score":0.7159000039100647},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.6848999857902527},{"id":"https://openalex.org/keywords/correlation-coefficient","display_name":"Correlation coefficient","score":0.5271999835968018},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.47679999470710754},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4546999931335449},{"id":"https://openalex.org/keywords/quality-score","display_name":"Quality Score","score":0.451200008392334},{"id":"https://openalex.org/keywords/grading","display_name":"Grading (engineering)","score":0.42239999771118164},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.3962000012397766},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3950999975204468},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.375900000333786}],"concepts":[{"id":"https://openalex.org/C30703548","wikidata":"https://www.wikidata.org/wiki/Q1757673","display_name":"Sobel operator","level":5,"score":0.7159000039100647},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.6848999857902527},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6274999976158142},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6097000241279602},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.5271999835968018},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.47679999470710754},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4546999931335449},{"id":"https://openalex.org/C2779346075","wikidata":"https://www.wikidata.org/wiki/Q7268763","display_name":"Quality Score","level":3,"score":0.451200008392334},{"id":"https://openalex.org/C2777286243","wikidata":"https://www.wikidata.org/wiki/Q5591926","display_name":"Grading (engineering)","level":2,"score":0.42239999771118164},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3962000012397766},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3950999975204468},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3912000060081482},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.375900000333786},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3682999908924103},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36230000853538513},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.35280001163482666},{"id":"https://openalex.org/C3020001037","wikidata":"https://www.wikidata.org/wiki/Q836575","display_name":"Quality assessment","level":3,"score":0.35190001130104065},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.3287000060081482},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.31619998812675476},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.31209999322891235},{"id":"https://openalex.org/C159744936","wikidata":"https://www.wikidata.org/wiki/Q1126730","display_name":"Spearman's rank correlation coefficient","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30889999866485596},{"id":"https://openalex.org/C19609008","wikidata":"https://www.wikidata.org/wiki/Q2138203","display_name":"Region of interest","level":2,"score":0.30160000920295715},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2896000146865845},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.27090001106262207},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C85461838","wikidata":"https://www.wikidata.org/wiki/Q7100785","display_name":"Ordinal data","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C101601086","wikidata":"https://www.wikidata.org/wiki/Q3753228","display_name":"Rank correlation","level":2,"score":0.258899986743927},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.25760000944137573},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.2572000026702881},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.25459998846054077},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.25279998779296875},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.25110000371932983},{"id":"https://openalex.org/C163864269","wikidata":"https://www.wikidata.org/wiki/Q1107106","display_name":"Cohen's kappa","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.10372","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10372","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.2606.10372","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10372","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":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.9102824926376343}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"abdominal":[1],"CT":[2,21],"imaging,":[3],"developing":[4],"a":[5,31],"low-dose,":[6],"no-reference":[7],"image":[8,22,67,168],"quality":[9,23,53,68,71,169],"assessment":[10],"(No-reference":[11],"IQA)":[12],"model":[13],"that":[14,178],"mimics":[15],"doctors'":[16],"reading":[17,43],"habits":[18],"for":[19],"evaluating":[20],"has":[24],"significant":[25],"practical":[26],"value.":[27],"This":[28],"paper":[29],"proposes":[30],"novel":[32],"deep":[33],"learning-based":[34],"framework,":[35],"ClinReadNet,":[36],"whose":[37],"design":[38],"aligns":[39],"with":[40,213],"the":[41,50,70,75,80,94,97,109,134,144,157,165,174,179,183,188,214],"clinical":[42,81],"logic":[44],"of":[45,74,85,113,126,147,167,190,216],"radiologists:":[46],"first,":[47],"it":[48,132],"introduces":[49],"Sobel":[51],"ordinal":[52],"network":[54],"(SOQN)":[55],"module,":[56,105],"which":[57,106,142],"can":[58],"simultaneously":[59],"focus":[60],"on":[61,173],"edge":[62],"details":[63,89],"highly":[64],"relevant":[65],"to":[66,118,156],"and":[69,90,121,150,201,210],"distribution":[72],"pattern":[73],"entire":[76],"image,":[77],"accurately":[78,122],"matching":[79],"image-reading":[82,111],"judgment":[83],"habit":[84],"\"considering":[86],"both":[87],"local":[88,119],"overall":[91,116],"context\";":[92],"second,":[93],"framework":[95],"integrates":[96],"(shifted)":[98],"window":[99],"multi-scale":[100],"temperature":[101],"multi-head":[102],"self-attention":[103],"((S)W-MTMSA)":[104],"further":[107],"replicates":[108],"radiologists'":[110],"process":[112],"shifting":[114],"from":[115],"scanning":[117],"focusing,":[120],"locks":[123],"in":[124],"regions":[125],"interest":[127],"through":[128],"multi-sharpness":[129],"attention;":[130],"third,":[131],"designs":[133],"hierarchical":[135],"ranked":[136],"probability":[137],"score":[138],"(HRPS)":[139],"loss":[140],"function,":[141],"combines":[143],"dual":[145],"logics":[146],"coarse":[148],"classification":[149],"fine":[151],"classification,":[152],"while":[153],"paying":[154],"attention":[155],"distance":[158],"information":[159],"between":[160],"grading":[161],"labels,":[162],"effectively":[163],"improving":[164],"performance":[166],"assessment.":[170],"Experiments":[171],"conducted":[172],"LDCTIQAG2023":[175],"dataset":[176],"show":[177],"proposed":[180],"method":[181],"achieves":[182],"current":[184],"state-of-the-art":[185],"(SOTA)":[186],"performance:":[187],"values":[189,219],"Pearson's":[191],"linear":[192],"correlation":[193,198,204],"coefficient":[194,199,205],"(PLCC),":[195],"Spearman's":[196],"rank-order":[197,203],"(SROCC),":[200],"Kendall's":[202],"(KROCC)":[206],"reach":[207],"0.9507,":[208],"0.9554,":[209],"0.8629":[211],"respectively,":[212],"sum":[215],"their":[217],"absolute":[218],"(Score)":[220],"being":[221],"2.7690,":[222],"outperforming":[223],"existing":[224],"methods.":[225]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-11T00:00:00"}
