{"id":"https://openalex.org/W7160334055","doi":"https://doi.org/10.1109/wacv61042.2026.00743","title":"MedPEFT-CL: Dual-Phase Parameter-Efficient Continual Learning with Medical Semantic Adapter and Bidirectional Memory Consolidation","display_name":"MedPEFT-CL: Dual-Phase Parameter-Efficient Continual Learning with Medical Semantic Adapter and Bidirectional Memory Consolidation","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W7160334055","doi":"https://doi.org/10.1109/wacv61042.2026.00743"},"language":null,"primary_location":{"id":"doi:10.1109/wacv61042.2026.00743","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00743","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5014687378","display_name":"Ziyuan Gao","orcid":"https://orcid.org/0009-0005-8092-5576"},"institutions":[{"id":"https://openalex.org/I2800173700","display_name":"UCL Australia","ror":"https://ror.org/024atcf19","country_code":"AU","type":"education","lineage":["https://openalex.org/I2800173700"]},{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["AU","GB"],"is_corresponding":false,"raw_author_name":"Ziyuan Gao","raw_affiliation_strings":["University College,London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University College,London","institution_ids":["https://openalex.org/I2800173700","https://openalex.org/I45129253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5106524705","display_name":"Philippe Morel","orcid":"https://orcid.org/0000-0002-9847-3409"},"institutions":[{"id":"https://openalex.org/I2800173700","display_name":"UCL Australia","ror":"https://ror.org/024atcf19","country_code":"AU","type":"education","lineage":["https://openalex.org/I2800173700"]},{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["AU","GB"],"is_corresponding":false,"raw_author_name":"Philippe Morel","raw_affiliation_strings":["University College,London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University College,London","institution_ids":["https://openalex.org/I2800173700","https://openalex.org/I45129253"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.462629,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7699","last_page":"7708"},"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":0.8830000162124634,"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":0.8830000162124634,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.04749999940395355,"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/T10028","display_name":"Topic Modeling","score":0.006399999838322401,"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/adapter","display_name":"Adapter (computing)","score":0.45419999957084656},{"id":"https://openalex.org/keywords/consolidation","display_name":"Consolidation (business)","score":0.375},{"id":"https://openalex.org/keywords/semantic-memory","display_name":"Semantic memory","score":0.28040000796318054},{"id":"https://openalex.org/keywords/memory-consolidation","display_name":"Memory consolidation","score":0.2705000042915344},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.2590000033378601}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.722599983215332},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5113000273704529},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.45419999957084656},{"id":"https://openalex.org/C2776014549","wikidata":"https://www.wikidata.org/wiki/Q3050847","display_name":"Consolidation (business)","level":2,"score":0.375},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3422999978065491},{"id":"https://openalex.org/C197914299","wikidata":"https://www.wikidata.org/wiki/Q18650","display_name":"Semantic memory","level":3,"score":0.28040000796318054},{"id":"https://openalex.org/C48455012","wikidata":"https://www.wikidata.org/wiki/Q2892593","display_name":"Memory consolidation","level":3,"score":0.2705000042915344},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.26089999079704285},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.24240000545978546}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv61042.2026.00743","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00743","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2008359794","https://openalex.org/W2021088830","https://openalex.org/W2285968993","https://openalex.org/W2473930607","https://openalex.org/W2560328367","https://openalex.org/W2560647685","https://openalex.org/W2915232829","https://openalex.org/W2953532875","https://openalex.org/W2963072899","https://openalex.org/W2963466845","https://openalex.org/W2982580298","https://openalex.org/W2991372685","https://openalex.org/W2997286550","https://openalex.org/W3202253200","https://openalex.org/W4296122590","https://openalex.org/W4385573131","https://openalex.org/W4386012715","https://openalex.org/W4386222752","https://openalex.org/W4391654261","https://openalex.org/W4392007284","https://openalex.org/W4396753437","https://openalex.org/W4402702936","https://openalex.org/W4403150216","https://openalex.org/W4406141311","https://openalex.org/W4407257596","https://openalex.org/W4414037010","https://openalex.org/W4417456879"],"related_works":[],"abstract_inverted_index":{"Medical":[0],"vision-language":[1,42,197],"segmentation":[2],"models":[3],"suffer":[4],"from":[5,167],"catastrophic":[6,165],"forgetting":[7,166,179],"when":[8],"adapting":[9],"to":[10],"new":[11,60,117,141],"anatomical":[12],"structures,":[13],"requiring":[14],"complete":[15],"retraining":[16],"that":[17,54,82,122,136,149,163],"limits":[18],"their":[19],"clinical":[20],"deployment.":[21],"Although":[22],"continual":[23,35,51,193],"learning":[24,36,52,58,80,139,194],"approaches":[25,37],"have":[26],"been":[27],"studied":[28],"for":[29,40,91,192],"various":[30],"applications,":[31],"targeted":[32],"research":[33],"on":[34,72],"specifically":[38],"designed":[39],"medical":[41,92,142,169,175,196],"tasks":[43,61,93,118],"remains":[44],"underexplored.":[45],"We":[46],"propose":[47],"MedPEFT-CL,":[48],"a":[49,68,99,109,131,145],"parameter-efficient":[50,89],"framework":[53,190],"addresses":[55],"both":[56],"efficient":[57,138],"of":[59,64,140],"and":[62,88,98,158,181],"preservation":[63],"previous":[65,168],"knowledge":[66,100],"through":[67,94],"dual-phase":[69,75],"architecture":[70,76],"based":[71],"CLIPSeg.":[73],"Our":[74,126],"features":[77],"an":[78],"adaptive":[79],"phase":[81,102],"employs":[83],"semantic":[84],"similarity-based":[85],"adapter":[86,133],"allocation":[87,134],"fine-tuning":[90],"prompt":[95],"similarity":[96],"analysis,":[97],"consolidation":[101,112],"employing":[103],"bi-directional":[104],"Fisher-memory":[105,161],"coordination.":[106],"This":[107],"creates":[108],"reinforcing":[110],"cycle:":[111],"directs":[113],"replay":[114],"priorities":[115],"while":[116,154],"provide":[119],"challenging":[120],"samples":[121],"improve":[123],"retention":[124,183],"strategies.":[125],"key":[127],"contributions":[128],"are:":[129],"(1)":[130],"semantic-driven":[132],"mechanism":[135],"enables":[137],"tasks,":[143],"(2)":[144],"bi-modal":[146],"LoRA":[147],"adaptation":[148],"significantly":[150],"reduces":[151],"trainable":[152],"parameters":[153],"maintaining":[155],"cross-modal":[156],"learning,":[157],"(3)":[159],"bidirectional":[160],"coordination":[162],"prevents":[164],"tasks.":[170],"Extensive":[171],"experiments":[172],"across":[173],"diverse":[174],"datasets":[176],"demonstrate":[177],"superior":[178],"mitigation":[180],"performance":[182],"with":[184],"minimal":[185],"parameter":[186],"overhead,":[187],"making":[188],"the":[189],"effective":[191],"in":[195],"scenarios.":[198]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-05-06T00:00:00"}