{"id":"https://openalex.org/W7160940002","doi":"https://doi.org/10.48550/arxiv.2605.08210","title":"Harmonized Feature Conditioning and Frequency-Prompt Personalization for Multi-Rater Medical Segmentation","display_name":"Harmonized Feature Conditioning and Frequency-Prompt Personalization for Multi-Rater Medical Segmentation","publication_year":2026,"publication_date":"2026-05-06","ids":{"openalex":"https://openalex.org/W7160940002","doi":"https://doi.org/10.48550/arxiv.2605.08210"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.08210","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.08210","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.2605.08210","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108817321","display_name":"Sanaz Karimijafarbigloo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Karimijafarbigloo, Sanaz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120539553","display_name":"Armin Khosravi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khosravi, Armin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135981397","display_name":"Alireza Kheyrkhah","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kheyrkhah, Alireza","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087512747","display_name":"Reza Azad","orcid":"https://orcid.org/0000-0002-4772-2161"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Azad, Reza","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135919161","display_name":"Mauricio Reyes","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Reyes, Mauricio","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5064747056","display_name":"Dorit Merhof","orcid":"https://orcid.org/0000-0002-1672-2185"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Merhof, Dorit","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/T10862","display_name":"AI in cancer detection","score":0.20509999990463257,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10862","display_name":"AI in cancer detection","score":0.20509999990463257,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T14510","display_name":"Medical Imaging and Analysis","score":0.09860000014305115,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.07769999653100967,"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/probabilistic-logic","display_name":"Probabilistic logic","score":0.632099986076355},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6098999977111816},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5950999855995178},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.5390999913215637},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4603999853134155},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.4465000033378601},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.39820000529289246},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3961000144481659},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.38440001010894775}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7294999957084656},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.661300003528595},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.632099986076355},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6098999977111816},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5950999855995178},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.5390999913215637},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4603999853134155},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.4465000033378601},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4259999990463257},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.39820000529289246},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3961000144481659},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.38440001010894775},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.38359999656677246},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.37470000982284546},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3555999994277954},{"id":"https://openalex.org/C159423971","wikidata":"https://www.wikidata.org/wiki/Q177251","display_name":"Associative property","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.2856000065803528},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.2847000062465668},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2806999981403351},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2711000144481659},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25929999351501465},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.2572000026702881},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2556000053882599},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.2531000077724457},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.25290000438690186},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.08210","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.08210","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.2605.08210","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.08210","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":[{"score":0.4270092248916626,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multi-rater":[0],"medical":[1],"image":[2],"segmentation":[3],"captures":[4],"the":[5,102,134,176],"inherent":[6],"ambiguity":[7],"of":[8],"clinical":[9,203],"interpretation,":[10],"where":[11,143,148],"diagnostic":[12],"boundaries":[13],"vary":[14],"across":[15],"experts":[16,144],"and":[17,38,60,71,110,146,154,159,166,187,198],"imaging":[18],"devices.":[19],"Existing":[20],"approaches":[21],"often":[22],"reduce":[23],"this":[24],"diversity":[25,142],"to":[26,76,105,119],"consensus":[27,147],"labels":[28],"or":[29],"treat":[30],"rater":[31],"differences":[32],"as":[33,195],"noise,":[34],"resulting":[35],"in":[36,101,184,189],"overconfident":[37],"poorly":[39],"calibrated":[40],"models.":[41],"We":[42],"propose":[43],"a":[44,94,127,196],"harmonized":[45,117],"probabilistic":[46],"framework":[47],"that":[48,81,99],"disentangles":[49],"acquisition":[50],"artifacts":[51,70],"from":[52],"genuine":[53],"annotator":[54],"variability":[55],"through":[56],"adaptive":[57],"feature":[58,74],"conditioning":[59],"frequency-domain":[61],"personalization.":[62],"A":[63],"lightweight":[64],"Harmonizer":[65],"Network":[66],"implicitly":[67],"models":[68],"scanner-specific":[69],"performs":[72],"dynamic":[73],"modulation":[75],"standardize":[77],"latent":[78],"representations,":[79],"ensuring":[80],"uncertainty":[82],"reflects":[83],"anatomy":[84],"rather":[85],"than":[86],"noise.":[87],"To":[88],"represent":[89],"rater-specific":[90],"styles,":[91],"we":[92],"introduce":[93],"novel":[95],"High-Frequency":[96],"Prompt":[97],"Modules":[98],"operate":[100],"spectral":[103],"domain":[104],"encode":[106],"annotator-dependent":[107],"boundary":[108],"precision":[109],"textural":[111],"sensitivity.":[112],"These":[113],"prompts":[114],"adaptively":[115],"modulate":[116],"features":[118],"produce":[120],"personalized":[121],"yet":[122],"anatomically":[123],"consistent":[124],"segmentations.":[125],"Furthermore,":[126],"Generalized":[128],"Energy":[129],"Distance":[130],"based":[131],"regularization":[132],"aligns":[133],"generative":[135],"distribution":[136],"with":[137,162],"empirical":[138],"annotation":[139],"variability,":[140],"promoting":[141],"disagree":[145],"they":[149],"converge.":[150],"Experiments":[151],"on":[152,171],"LIDC-IDRI":[153],"NPC-170":[155],"show":[156],"SOTA":[157],"aggregated":[158],"individualized":[160],"segmentation,":[161],"notable":[163],"GED":[164],"reductions":[165],"improved":[167],"Dice":[168],"scores,":[169],"especially":[170],"noisy":[172],"cases.":[173],"Beyond":[174],"accuracy,":[175],"model":[177],"exhibits":[178],"clinically":[179],"meaningful":[180],"uncertainty.":[181],"Confidence":[182],"rises":[183],"agreement":[185],"regions":[186],"declines":[188],"ambiguous":[190],"areas,":[191],"supporting":[192],"its":[193],"use":[194],"reliable":[197],"interpretable":[199],"tool":[200],"for":[201],"multi-expert":[202],"workflows.":[204]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-13T00:00:00"}
