{"id":"https://openalex.org/W4406264511","doi":"https://doi.org/10.1109/euvip61797.2024.10772877","title":"MRIShift: Disentangled Representation Learning for 3D MRI Lesion Segmentation Under Distributional Shifts","display_name":"MRIShift: Disentangled Representation Learning for 3D MRI Lesion Segmentation Under Distributional Shifts","publication_year":2024,"publication_date":"2024-09-08","ids":{"openalex":"https://openalex.org/W4406264511","doi":"https://doi.org/10.1109/euvip61797.2024.10772877"},"language":"en","primary_location":{"id":"doi:10.1109/euvip61797.2024.10772877","is_oa":false,"landing_page_url":"https://doi.org/10.1109/euvip61797.2024.10772877","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 12th European Workshop on Visual Information Processing (EUVIP)","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/A5015650372","display_name":"Umaima Rahman","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113480","display_name":"Mohamed bin Zayed University of Artificial Intelligence","ror":"https://ror.org/0258gkt32","country_code":"AE","type":"education","lineage":["https://openalex.org/I4210113480"]}],"countries":["AE"],"is_corresponding":false,"raw_author_name":"Umaima Rahman","raw_affiliation_strings":["Mohamed Bin Zayed University of Artificial Intelligence,Computer Vision Department,Abu Dhabi,UAE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mohamed Bin Zayed University of Artificial Intelligence,Computer Vision Department,Abu Dhabi,UAE","institution_ids":["https://openalex.org/I4210113480"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088257057","display_name":"Guangyi Chen","orcid":"https://orcid.org/0000-0001-7542-5378"},"institutions":[{"id":"https://openalex.org/I4210113480","display_name":"Mohamed bin Zayed University of Artificial Intelligence","ror":"https://ror.org/0258gkt32","country_code":"AE","type":"education","lineage":["https://openalex.org/I4210113480"]}],"countries":["AE"],"is_corresponding":false,"raw_author_name":"Guangyi Chen","raw_affiliation_strings":["Mohamed Bin Zayed University of Artificial Intelligence,Computer Vision Department,Abu Dhabi,UAE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mohamed Bin Zayed University of Artificial Intelligence,Computer Vision Department,Abu Dhabi,UAE","institution_ids":["https://openalex.org/I4210113480"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022021576","display_name":"Kun Zhang","orcid":"https://orcid.org/0000-0002-0898-5807"},"institutions":[{"id":"https://openalex.org/I4210113480","display_name":"Mohamed bin Zayed University of Artificial Intelligence","ror":"https://ror.org/0258gkt32","country_code":"AE","type":"education","lineage":["https://openalex.org/I4210113480"]}],"countries":["AE"],"is_corresponding":false,"raw_author_name":"Kun Zhang","raw_affiliation_strings":["Mohamed Bin Zayed University of Artificial Intelligence,Computer Vision Department,Abu Dhabi,UAE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mohamed Bin Zayed University of Artificial Intelligence,Computer Vision Department,Abu Dhabi,UAE","institution_ids":["https://openalex.org/I4210113480"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210113480"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.991100013256073,"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"}},"topics":[{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.991100013256073,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9764000177383423,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9692000150680542,"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/representation","display_name":"Representation (politics)","score":0.714786171913147},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7069094181060791},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5932385921478271},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.589935302734375},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38967716693878174},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.32870638370513916}],"concepts":[{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.714786171913147},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7069094181060791},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5932385921478271},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.589935302734375},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38967716693878174},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32870638370513916},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/euvip61797.2024.10772877","is_oa":false,"landing_page_url":"https://doi.org/10.1109/euvip61797.2024.10772877","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 12th European Workshop on Visual Information Processing (EUVIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6800000071525574,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"In":[0,53],"the":[1,14,20,69,72,119,145,152,165,186,215,218,223],"clinical":[2],"environment,":[3],"heterogeneity":[4],"of":[5,22,71,154,217],"data":[6,15,112,120,194],"coming":[7],"from":[8,113],"different":[9,81,114,130],"centers":[10],"poses":[11],"challenges":[12],"where":[13],"may":[16],"not":[17,49],"conform":[18],"to":[19,35,67,80,95,100,150],"assumption":[21],"being":[23],"independent":[24],"and":[25,132,178,202],"identically":[26],"distributed":[27],"(i.i.d).":[28],"As":[29],"a":[30,171,203],"result,":[31],"it":[32,47,92],"is":[33,93],"essential":[34,94],"develop":[36],"an":[37,58],"approach":[38,62],"that":[39,126,135,140,175],"can":[40],"generate":[41],"better":[42],"predictions":[43],"even":[44],"on":[45,108,191],"samples":[46,78],"did":[48],"encounter":[50],"during":[51,84],"training.":[52,85],"this":[54],"paper,":[55],"we":[56,106,148],"propose":[57],"unsupervised":[59],"domain":[60,141],"adaptation":[61],"using":[63],"disentangled":[64],"representation":[65],"learning":[66],"enhance":[68],"transferability":[70],"3D":[73],"segmentation":[74,183,197],"model":[75,219],"for":[76],"medical":[77,89,193],"belonging":[79],"distributions":[82,163,201],"unseen":[83],"When":[86],"dealing":[87],"with":[88,182],"image":[90,177],"segmentation,":[91],"understand":[96],"how":[97],"features":[98],"relate":[99],"voxel-wise":[101],"labels.":[102],"To":[103],"achieve":[104],"this,":[105],"focus":[107],"minimizing":[109],"changes":[110],"between":[111],"sources.":[115],"This":[116,157],"involves":[117],"organizing":[118],"into":[121],"two":[122],"categories:":[123],"invariant":[124],"components":[125,134],"remain":[127],"consistent":[128],"across":[129],"sources,":[131],"variant":[133],"exhibit":[136],"changes.":[137],"By":[138],"ensuring":[139],"shifts":[142],"primarily":[143],"affect":[144],"changing":[146],"components,":[147],"aim":[149],"preserve":[151],"identifiability":[153],"latent":[155],"variables.":[156],"helps":[158],"in":[159,207,225],"identifying":[160],"joint":[161],"data-label":[162],"within":[164,199],"target":[166],"domain.":[167],"We":[168],"also":[169],"introduce":[170],"new":[172],"loss":[173],"function":[174],"integrates":[176],"feature":[179],"level":[180],"reconstruction":[181],"loss,":[184],"enhancing":[185],"model's":[187],"performance.":[188],"Our":[189],"experiments":[190],"real-world":[192],"demonstrate":[195],"improved":[196],"accuracy":[198],"known":[200],"notable":[204],"5%":[205],"enhancement":[206],"handling":[208],"out-of-distribution":[209],"samples.":[210],"Additionally,":[211],"our":[212],"method":[213],"enhances":[214],"robustness":[216],"as":[220,229],"demonstrated":[221],"by":[222],"improvement":[224],"performance":[226],"metrics":[227],"such":[228],"R-AUC.":[230]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}