{"id":"https://openalex.org/W7134910188","doi":"https://doi.org/10.48550/arxiv.2603.08906","title":"Multi-Kernel Gated Decoder Adapters for Robust Multi-Task Thyroid Ultrasound under Cross-Center Shift","display_name":"Multi-Kernel Gated Decoder Adapters for Robust Multi-Task Thyroid Ultrasound under Cross-Center Shift","publication_year":2026,"publication_date":"2026-03-09","ids":{"openalex":"https://openalex.org/W7134910188","doi":"https://doi.org/10.48550/arxiv.2603.08906"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.08906","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.08906","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.08906","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5087611160","display_name":"Maziar Sabouri","orcid":"https://orcid.org/0000-0001-7525-8952"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Sabouri, Maziar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054705753","display_name":"Nourhan Bayasi","orcid":"https://orcid.org/0000-0003-4653-6081"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bayasi, Nourhan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128726516","display_name":"Arman Rahmim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rahmim, Arman","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5087611160"],"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.3440000116825104,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.3440000116825104,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/T10329","display_name":"Thyroid Cancer Diagnosis and Treatment","score":0.12349999696016312,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"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/T10862","display_name":"AI in cancer detection","score":0.09730000048875809,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5548999905586243},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4830999970436096},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.4357999861240387},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39879998564720154},{"id":"https://openalex.org/keywords/malignancy","display_name":"Malignancy","score":0.36090001463890076},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.3467999994754791},{"id":"https://openalex.org/keywords/gating","display_name":"Gating","score":0.33390000462532043},{"id":"https://openalex.org/keywords/ultrasound","display_name":"Ultrasound","score":0.3310999870300293},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.32120001316070557}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6478999853134155},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5548999905586243},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5224000215530396},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4830999970436096},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.4357999861240387},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39879998564720154},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.36419999599456787},{"id":"https://openalex.org/C2779399171","wikidata":"https://www.wikidata.org/wiki/Q1483951","display_name":"Malignancy","level":2,"score":0.36090001463890076},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.3467999994754791},{"id":"https://openalex.org/C194544171","wikidata":"https://www.wikidata.org/wiki/Q21105679","display_name":"Gating","level":2,"score":0.33390000462532043},{"id":"https://openalex.org/C143753070","wikidata":"https://www.wikidata.org/wiki/Q162564","display_name":"Ultrasound","level":2,"score":0.3310999870300293},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.32120001316070557},{"id":"https://openalex.org/C2779761222","wikidata":"https://www.wikidata.org/wiki/Q826522","display_name":"Thyroid cancer","level":3,"score":0.3174000084400177},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.30720001459121704},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.303600013256073},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.30059999227523804},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2955999970436096},{"id":"https://openalex.org/C2780170424","wikidata":"https://www.wikidata.org/wiki/Q229399","display_name":"3D ultrasound","level":3,"score":0.2924000024795532},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.28619998693466187},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.271699994802475},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C83248878","wikidata":"https://www.wikidata.org/wiki/Q344000","display_name":"Active appearance model","level":3,"score":0.26080000400543213},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.25200000405311584}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.08906","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.08906","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.08906","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.08906","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":"article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.5055378079414368}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Thyroid":[0],"ultrasound":[1],"(US)":[2],"automation":[3],"couples":[4],"two":[5,130],"competing":[6],"requirements:":[7],"global,":[8],"geometry-driven":[9],"reasoning":[10,17],"for":[11,18,78],"nodule":[12],"delineation":[13],"and":[14,54,59,84,104,118,162],"local,":[15],"texture-driven":[16],"malignancy":[19,79],"risk":[20],"assessment.":[21],"Under":[22],"cross-center":[23,137],"domain":[24],"shift,":[25],"these":[26],"cues":[27,77],"degrade":[28],"asymmetrically,":[29],"yet":[30],"most":[31],"multi-task":[32,159],"pipelines":[33],"rely":[34],"on":[35],"a":[36,61,93,105],"single":[37],"shared":[38],"backbone,":[39],"often":[40],"inducing":[41],"negative":[42],"transfer.":[43],"In":[44],"this":[45,49,88],"paper,":[46],"we":[47,91],"characterize":[48],"interference":[50],"across":[51],"CNN":[52,146],"(ResNet34)":[53],"medical":[55],"ViT":[56],"(MedSAM)":[57],"backbones,":[58],"observe":[60],"consistent":[62],"trend:":[63],"ViTs":[64],"transfer":[65],"geometric":[66],"priors":[67],"that":[68],"benefit":[69],"segmentation,":[70],"whereas":[71],"CNNs":[72],"more":[73],"reliably":[74],"preserve":[75],"texture":[76],"discrimination":[80],"under":[81],"strong":[82],"shift":[83],"artifacts.":[85],"Motivated":[86],"by":[87],"failure":[89],"mode,":[90],"propose":[92],"lightweight":[94],"family":[95],"of":[96],"decoder-side":[97],"adapters,":[98],"the":[99,133,145],"Multi-Kernel":[100],"Gated":[101],"Adapter":[102],"(MKGA)":[103],"residual":[106],"variant":[107],"(ResMKGA),":[108],"which":[109],"refine":[110],"multi-scale":[111],"skip":[112],"features":[113],"using":[114],"complementary":[115],"receptive":[116],"fields":[117],"apply":[119],"semantic,":[120],"context-conditioned":[121],"gating":[122],"to":[123,157],"suppress":[124],"artifact-prone":[125],"content":[126],"before":[127],"fusion.":[128],"Across":[129],"US":[131],"benchmarks,":[132],"proposed":[134],"adapters":[135],"improve":[136],"robustness:":[138],"they":[139],"strengthen":[140],"out-of-domain":[141],"segmentation":[142],"and,":[143],"in":[144,151],"setting,":[147],"yield":[148],"clear":[149],"gains":[150],"clinical":[152],"TI-RADS":[153],"diagnostic":[154],"accuracy":[155],"compared":[156],"standard":[158],"baselines.":[160],"Code":[161],"models":[163],"will":[164],"be":[165],"released.":[166]},"counts_by_year":[],"updated_date":"2026-03-12T06:18:43.230356","created_date":"2026-03-12T00:00:00"}