{"id":"https://openalex.org/W7117478646","doi":"https://doi.org/10.1109/dicta68720.2025.11302435","title":"Causally Guided Gaussian Perturbations for Out-of-Distribution Generalization in Medical Imaging","display_name":"Causally Guided Gaussian Perturbations for Out-of-Distribution Generalization in Medical Imaging","publication_year":2025,"publication_date":"2025-12-03","ids":{"openalex":"https://openalex.org/W7117478646","doi":"https://doi.org/10.1109/dicta68720.2025.11302435"},"language":null,"primary_location":{"id":"doi:10.1109/dicta68720.2025.11302435","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta68720.2025.11302435","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","raw_type":"proceedings-article"},"type":"article","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/A5121516655","display_name":"Haoran Pei","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haoran Pei","raw_affiliation_strings":["Beihang University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beihang University,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5094351708","display_name":"Yuguang Yang","orcid":"https://orcid.org/0000-0002-6394-4645"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuguang Yang","raw_affiliation_strings":["Beihang University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beihang University,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100451435","display_name":"Kexin Liu","orcid":"https://orcid.org/0000-0002-0699-8452"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kexin Liu","raw_affiliation_strings":["Beihang University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beihang University,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019906977","display_name":"Baochang Zhang","orcid":"https://orcid.org/0000-0002-2615-787X"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baochang Zhang","raw_affiliation_strings":["Beihang University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beihang University,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5121516655"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.63945115,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.5544000267982483,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.5544000267982483,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.19990000128746033,"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":0.07079999893903732,"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/generalization","display_name":"Generalization","score":0.7760999798774719},{"id":"https://openalex.org/keywords/spurious-relationship","display_name":"Spurious relationship","score":0.7754999995231628},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5152000188827515},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.48559999465942383},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.41290000081062317},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.41100001335144043},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.39070001244544983},{"id":"https://openalex.org/keywords/perturbation","display_name":"Perturbation (astronomy)","score":0.38260000944137573}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.7760999798774719},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.7754999995231628},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6348999738693237},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5462999939918518},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5152000188827515},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.48559999465942383},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42590001225471497},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.41290000081062317},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.41100001335144043},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.39070001244544983},{"id":"https://openalex.org/C177918212","wikidata":"https://www.wikidata.org/wiki/Q803623","display_name":"Perturbation (astronomy)","level":2,"score":0.38260000944137573},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.3736000061035156},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.34139999747276306},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3384999930858612},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.33169999718666077},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32109999656677246},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.31619998812675476},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2994999885559082},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.2802000045776367},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27239999175071716}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dicta68720.2025.11302435","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta68720.2025.11302435","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2580866548","display_name":null,"funder_award_id":"6255000184","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3010619704","display_name":null,"funder_award_id":"L244043","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"},{"id":"https://openalex.org/G4339263879","display_name":null,"funder_award_id":"2023YFC3306401","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G934549851","display_name":null,"funder_award_id":"Z231100005923035","funder_id":"https://openalex.org/F4320325902","funder_display_name":"Beijing Municipal Science and Technology Commission"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null},{"id":"https://openalex.org/F4320325902","display_name":"Beijing Municipal Science and Technology Commission","ror":null},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2306289963","https://openalex.org/W2552414813","https://openalex.org/W2791655542","https://openalex.org/W2811374795","https://openalex.org/W2883386984","https://openalex.org/W2962758679","https://openalex.org/W2969278648","https://openalex.org/W2991979976","https://openalex.org/W3016970897","https://openalex.org/W3094502228","https://openalex.org/W3096609285","https://openalex.org/W3170841864","https://openalex.org/W4200191097","https://openalex.org/W4206706211","https://openalex.org/W4213019189","https://openalex.org/W4214519690","https://openalex.org/W4225356279","https://openalex.org/W4312252361","https://openalex.org/W4312836939","https://openalex.org/W4312908248","https://openalex.org/W4312919733","https://openalex.org/W4321505042","https://openalex.org/W4393157035","https://openalex.org/W4394683903"],"related_works":[],"abstract_inverted_index":{"Out-of-distribution":[0],"(OOD)":[1],"generalization":[2,68],"remains":[3],"a":[4,136],"central":[5],"challenge":[6],"in":[7,16],"deploying":[8],"deep":[9],"learning":[10],"models":[11,40],"to":[12,90,96,103],"realworld":[13],"scenarios,":[14],"particularly":[15],"domains":[17],"such":[18],"as":[19,135],"biomedical":[20],"images,":[21,76],"where":[22],"distribution":[23],"shifts":[24],"are":[25],"both":[26],"subtle":[27],"and":[28,93,140],"pervasive.":[29],"While":[30],"existing":[31],"methods":[32],"often":[33],"pursue":[34],"domain":[35],"invariance":[36],"through":[37],"complex":[38],"generative":[39],"or":[41],"adversarial":[42],"training,":[43],"these":[44],"approaches":[45],"may":[46],"overlook":[47],"the":[48,101,116,130],"underlying":[49],"causal":[50,80,133],"mechanisms":[51],"of":[52,132],"generalization.":[53,142],"In":[54],"this":[55],"work,":[56],"we":[57],"propose":[58],"Causally-Guided":[59],"Gaussian":[60],"Perturbations":[61],"(CGP)-a":[62],"lightweight":[63],"framework":[64],"that":[65],"enhances":[66],"OOD":[67,127],"by":[69,78],"injecting":[70],"spatially":[71],"varying":[72],"noise":[73],"into":[74],"input":[75],"guided":[77],"soft":[79],"masks":[81],"derived":[82],"from":[83],"Vision":[84],"Transformers.":[85],"By":[86],"applying":[87],"stronger":[88],"perturbations":[89],"background":[91],"regions":[92],"weaker":[94],"ones":[95],"foreground":[97],"areas,":[98],"CGP":[99],"encourages":[100],"model":[102],"rely":[104],"on":[105,115],"causally":[106],"relevant":[107],"features":[108],"rather":[109],"than":[110],"spurious":[111],"correlations.":[112],"Experimental":[113],"results":[114],"challenging":[117],"WILDS":[118],"benchmark":[119],"Camelyon17":[120],"demonstrate":[121],"consistent":[122],"performance":[123],"gains":[124],"over":[125],"state-of-the-art":[126],"baselines,":[128],"highlighting":[129],"potential":[131],"perturbation":[134],"tool":[137],"for":[138],"reliable":[139],"interpretable":[141]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-12-29T00:00:00"}