{"id":"https://openalex.org/W7160404797","doi":"https://doi.org/10.48550/arxiv.2605.03787","title":"A Robust Unsupervised Domain Adaptation Framework for Medical Image Classification Using RKHS-MMD","display_name":"A Robust Unsupervised Domain Adaptation Framework for Medical Image Classification Using RKHS-MMD","publication_year":2026,"publication_date":"2026-05-05","ids":{"openalex":"https://openalex.org/W7160404797","doi":"https://doi.org/10.48550/arxiv.2605.03787"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.03787","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03787","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.2605.03787","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085910571","display_name":"Sapna Sachan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sachan, Sapna","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068482436","display_name":"Rakesh Kumar Sanodiya","orcid":"https://orcid.org/0000-0002-9524-9284"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sanodiya, Rakesh Kumar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5043352470","display_name":"Amulya Kumar Mahto","orcid":"https://orcid.org/0000-0001-8389-5257"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mahto, Amulya Kumar","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.6678000092506409,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.6678000092506409,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.23989999294281006,"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/T11448","display_name":"Face recognition and analysis","score":0.015200000256299973,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.71670001745224},{"id":"https://openalex.org/keywords/reproducing-kernel-hilbert-space","display_name":"Reproducing kernel Hilbert space","score":0.5932000279426575},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.5746999979019165},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.536899983882904},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5001000165939331},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49570000171661377},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.48669999837875366},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.4708000123500824},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.46889999508857727}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.71670001745224},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6746000051498413},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6746000051498413},{"id":"https://openalex.org/C80884492","wikidata":"https://www.wikidata.org/wiki/Q3345678","display_name":"Reproducing kernel Hilbert space","level":3,"score":0.5932000279426575},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.5746999979019165},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.536899983882904},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5001000165939331},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49570000171661377},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.48669999837875366},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.4708000123500824},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4699000120162964},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.46889999508857727},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.43160000443458557},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.399399995803833},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.37439998984336853},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3698999881744385},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.36820000410079956},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.36340001225471497},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.32910001277923584},{"id":"https://openalex.org/C154874363","wikidata":"https://www.wikidata.org/wiki/Q3518464","display_name":"Medical classification","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2874999940395355},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C2776879701","wikidata":"https://www.wikidata.org/wiki/Q25048660","display_name":"Multiple kernel learning","level":4,"score":0.26910001039505005},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2572999894618988},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.2572000026702881}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.03787","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03787","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.2605.03787","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03787","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Labeling":[0],"medical":[1,11,28,97,118,154,165],"images":[2],"is":[3],"a":[4,66,131],"major":[5],"bottleneck":[6],"in":[7,146,162],"the":[8,44,77,91,141],"field":[9],"of":[10,46,79],"imaging,":[12],"as":[13],"it":[14,19],"requires":[15],"domain-specific":[16],"expertise,":[17],"and":[18,30,39,81,88,157],"gets":[20],"further":[21],"complicated":[22],"due":[23],"to":[24,95,134],"variability":[25],"across":[26],"different":[27,31,117],"centers":[29],"imaging":[32],"devices.":[33],"Such":[34],"heterogeneity":[35],"introduces":[36],"domain":[37,58],"shifts":[38],"modality":[40,148],"discrepancies,":[41],"which":[42,113],"limits":[43],"generalization":[45,94],"trained":[47,125],"models.":[48],"To":[49],"address":[50],"this":[51],"important":[52],"challenge,":[53],"we":[54,129],"propose":[55],"an":[56],"unsupervised":[57],"adaptation":[59],"framework":[60],"that":[61,136],"combines":[62],"transfer":[63],"learning":[64],"with":[65],"Reproducing":[67],"Kernel":[68],"Hilbert":[69],"Space":[70],"based":[71],"Maximum":[72,143],"Mean":[73,144],"Discrepancy":[74,145],"loss":[75],"for":[76,153],"alignment":[78],"source":[80],"target":[82],"domains.":[83],"By":[84],"jointly":[85],"optimizing":[86],"classification":[87,156],"RKHS-MMD":[89,137],"losses,":[90],"methodology":[92],"enhances":[93],"unannotated":[96],"datasets":[98],"while":[99],"diminishing":[100],"reliance":[101],"on":[102,108],"manual":[103],"annotation.":[104],"Experimental":[105],"evaluations":[106],"presented":[107],"two":[109],"chest":[110],"X-ray":[111],"datasets,":[112],"are":[114],"obtained":[115],"from":[116],"centers,":[119],"show":[120],"outstanding":[121],"improvements":[122],"over":[123],"models":[124],"without":[126],"adaptation.":[127],"Furthermore,":[128],"perform":[130],"comparative":[132],"study":[133],"see":[135],"performs":[138],"better":[139],"than":[140],"standard":[142],"reducing":[147],"gap,":[149],"emphasizing":[150],"its":[151,159],"effectiveness":[152],"image":[155],"also":[158],"strong":[160],"capability":[161],"advanced":[163],"AI-driven":[164],"diagnostics.":[166]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-07T00:00:00"}
