{"id":"https://openalex.org/W7161819162","doi":"https://doi.org/10.1109/isbi61048.2026.11515918","title":"Unsupervised Adaptation from FDG to PSMA PET/CT for 3D Lesion Detection Under Label Shift","display_name":"Unsupervised Adaptation from FDG to PSMA PET/CT for 3D Lesion Detection Under Label Shift","publication_year":2026,"publication_date":"2026-04-08","ids":{"openalex":"https://openalex.org/W7161819162","doi":"https://doi.org/10.1109/isbi61048.2026.11515918"},"language":null,"primary_location":{"id":"doi:10.1109/isbi61048.2026.11515918","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi61048.2026.11515918","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI)","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/A5136596192","display_name":"Xiaofeng Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xiaofeng Liu","raw_affiliation_strings":["Yale University,Yale Biomedical Imaging Institute and Department of Radiology &#x0026; Biomedical Imaging,New Haven,CT,USA,06520"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University,Yale Biomedical Imaging Institute and Department of Radiology &#x0026; Biomedical Imaging,New Haven,CT,USA,06520","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136517888","display_name":"Menghua Xia","orcid":null},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Menghua Xia","raw_affiliation_strings":["Yale University,Yale Biomedical Imaging Institute and Department of Radiology &#x0026; Biomedical Imaging,New Haven,CT,USA,06520"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University,Yale Biomedical Imaging Institute and Department of Radiology &#x0026; Biomedical Imaging,New Haven,CT,USA,06520","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072270163","display_name":"Yanis Chemli","orcid":"https://orcid.org/0000-0003-1592-5754"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanis Chemli","raw_affiliation_strings":["Yale University,Yale Biomedical Imaging Institute and Department of Radiology &#x0026; Biomedical Imaging,New Haven,CT,USA,06520"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University,Yale Biomedical Imaging Institute and Department of Radiology &#x0026; Biomedical Imaging,New Haven,CT,USA,06520","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136581403","display_name":"Georges El Fakhri","orcid":null},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Georges El Fakhri","raw_affiliation_strings":["Yale University,Yale Biomedical Imaging Institute and Department of Radiology &#x0026; Biomedical Imaging,New Haven,CT,USA,06520"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University,Yale Biomedical Imaging Institute and Department of Radiology &#x0026; Biomedical Imaging,New Haven,CT,USA,06520","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005615775","display_name":"C Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chi Liu","raw_affiliation_strings":["Yale University,Yale Biomedical Imaging Institute and Department of Radiology &#x0026; Biomedical Imaging,New Haven,CT,USA,06520"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University,Yale Biomedical Imaging Institute and Department of Radiology &#x0026; Biomedical Imaging,New Haven,CT,USA,06520","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101740893","display_name":"Jinsong Ouyang","orcid":"https://orcid.org/0000-0003-4245-170X"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jinsong Ouyang","raw_affiliation_strings":["Yale University,Yale Biomedical Imaging Institute and Department of Radiology &#x0026; Biomedical Imaging,New Haven,CT,USA,06520"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University,Yale Biomedical Imaging Institute and Department of Radiology &#x0026; Biomedical Imaging,New Haven,CT,USA,06520","institution_ids":["https://openalex.org/I32971472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5136596192"],"corresponding_institution_ids":["https://openalex.org/I32971472"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.93264003,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.6029000282287598,"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":0.6029000282287598,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.08540000021457672,"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/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.04639999940991402,"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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33629998564720154},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.32330000400543213},{"id":"https://openalex.org/keywords/positron-emission-tomography","display_name":"Positron emission tomography","score":0.29989999532699585},{"id":"https://openalex.org/keywords/lesion","display_name":"Lesion","score":0.2939999997615814}],"concepts":[{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.4733000099658966},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3874000012874603},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.36629998683929443},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33629998564720154},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.32330000400543213},{"id":"https://openalex.org/C136229726","wikidata":"https://www.wikidata.org/wiki/Q327092","display_name":"Biomedical engineering","level":1,"score":0.30959999561309814},{"id":"https://openalex.org/C2775842073","wikidata":"https://www.wikidata.org/wiki/Q208376","display_name":"Positron emission tomography","level":2,"score":0.29989999532699585},{"id":"https://openalex.org/C2781156865","wikidata":"https://www.wikidata.org/wiki/Q827023","display_name":"Lesion","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C2989005","wikidata":"https://www.wikidata.org/wiki/Q214963","display_name":"Nuclear medicine","level":1,"score":0.27090001106262207},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.26579999923706055},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.2628999948501587}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi61048.2026.11515918","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi61048.2026.11515918","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4770164485","display_name":null,"funder_award_id":"R01CA275188,P41EB022544,R21EB034911,R01CA296305,R01CA290745","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"}],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1996248547","https://openalex.org/W2963351448","https://openalex.org/W2985406498","https://openalex.org/W3154422954","https://openalex.org/W3183988645","https://openalex.org/W3190537960","https://openalex.org/W3202823735","https://openalex.org/W4292198371","https://openalex.org/W4300979672","https://openalex.org/W4315435776","https://openalex.org/W4318484049","https://openalex.org/W4405788188","https://openalex.org/W4411917516","https://openalex.org/W7148502318"],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1,63],"work,":[2],"we":[3,67,115],"propose":[4],"an":[5,88,191],"unsupervised":[6],"domain":[7,77,125],"adaptation":[8,34],"(UDA)":[9],"framework":[10],"for":[11,62,97,112],"3D":[12],"volumetric":[13],"lesion":[14,41,128,185],"detection":[15,71],"that":[16,57,182],"adapts":[17],"a":[18,108],"detector":[19],"trained":[20],"on":[21,140,145],"labeled":[22,146,154],"FDG":[23,155],"PET/CT":[24],"to":[25,121,157,194],"unlabeled":[26],"PSMA":[27,99,141],"PET/CT.":[28],"Beyond":[29],"covariate":[30],"shift,":[31],"cross":[32],"tracer":[33],"also":[35],"exhibits":[36],"label":[37,64],"shift":[38],"in":[39],"both":[40,167],"size":[42,117,188],"composition":[43,189],"and":[44,60,84,101,142,169,175,187],"the":[45,70,122,163,172],"number":[46],"of":[47,107],"lesions":[48,100],"per":[49],"subject.":[50],"We":[51],"introduce":[52],"self-training":[53,131,177],"with":[54,87,136],"two":[55],"mechanisms":[56],"explicitly":[58],"model":[59],"compensate":[61],"shift.":[65],"First,":[66],"adaptively":[68],"adjust":[69],"anchor":[72,95],"shapes":[73],"by":[74],"re-estimating":[75],"target":[76,124,184],"box":[78,103],"scales":[79],"from":[80,152],"selected":[81],"pseudo":[82,138],"labels":[83],"updating":[85],"anchors":[86],"exponential":[89],"moving":[90],"average.":[91],"This":[92],"increases":[93],"positive":[94],"coverage":[96],"small":[98],"stabilizes":[102],"regression.":[104],"Second,":[105],"instead":[106],"fixed":[109],"confidence":[110],"threshold":[111],"pseudo-label":[113],"selection,":[114],"allocate":[116],"bin-wise":[118],"quotas":[119],"according":[120],"estimated":[123],"histogram":[126],"over":[127,171],"volumes.":[129],"The":[130],"alternates":[132],"between":[133],"supervised":[134,143],"learning":[135,144],"prior-guided":[137],"labeling":[139],"FDG.":[147],"On":[148],"AutoPET":[149],"2024,":[150],"adapting":[151],"501":[153],"studies":[156],"369<sup":[158],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[159],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">18</sup>":[160],"F-PSMA":[161],"studies,":[162],"proposed":[164],"method":[165],"improves":[166],"AP":[168],"FROC":[170],"source-only":[173],"baseline":[174],"conventional":[176],"without":[178],"label-shift":[179],"mitigation,":[180],"indicating":[181],"modeling":[183],"prevalence":[186],"is":[190],"effective":[192],"path":[193],"robust":[195],"cross-tracer":[196],"detection.":[197]},"counts_by_year":[],"updated_date":"2026-05-22T06:13:13.366637","created_date":"2026-05-21T00:00:00"}