{"id":"https://openalex.org/W7213742867","doi":"https://doi.org/10.3390/brainsci16111191","title":"MedBackdoorGuard: Detecting and Mitigating Backdoor Attacks in Deep Learning Models for MRI-Based Alzheimer\u2019s Disease and Mild Cognitive Impairment Diagnosis","display_name":"MedBackdoorGuard: Detecting and Mitigating Backdoor Attacks in Deep Learning Models for MRI-Based Alzheimer\u2019s Disease and Mild Cognitive Impairment Diagnosis","publication_year":2026,"publication_date":"2026-10-30","ids":{"openalex":"https://openalex.org/W7213742867","doi":"https://doi.org/10.3390/brainsci16111191"},"language":"en","primary_location":{"id":"doi:10.3390/brainsci16111191","doi":"https://doi.org/10.3390/brainsci16111191","is_oa":true,"landing_page_url":"https://doi.org/10.3390/brainsci16111191","pdf_url":null,"source":{"id":"https://openalex.org/S122317144","display_name":"Brain Sciences","issn_l":"2076-3425","issn":["2076-3425"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-scientific","cwts-core","doaj","norway-1","poland-100","russia-white-list-3","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Brain Sciences","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/brainsci16111191","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111140051","display_name":"Syed Murtoza Mushrul Pasha","orcid":"https://orcid.org/0009-0004-1190-7633","observed_orcids":["https://orcid.org/0009-0004-1190-7633"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Syed Murtoza Mushrul Pasha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5098903239","display_name":"Shahidur Rahoman Sohag","orcid":"https://orcid.org/0009-0009-1567-6058","observed_orcids":["https://orcid.org/0009-0009-1567-6058"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shahidur Rahoman Sohag","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5023388903","display_name":"Hossny Ghanem","orcid":"https://orcid.org/0000-0002-8091-5504","observed_orcids":["https://orcid.org/0000-0002-8091-5504"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Hossny Ghanem","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5023388903"],"corresponding_institution_ids":[],"apc_list":{"value":2200,"currency":"CHF","value_usd":2654},"apc_paid":{"value":2200,"currency":"CHF","value_usd":2654},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.65391645,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"16","issue":"11","first_page":"1191","last_page":"1191"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9994999766349792,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9994999766349792,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.00019999999494757503,"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/T11424","display_name":"Security and Verification in Computing","score":9.999999747378752e-05,"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/backdoor-attacks","display_name":"backdoor attacks","score":0.9679999947547913},{"id":"https://openalex.org/keywords/backdoor-detection","display_name":"backdoor detection","score":0.7450000047683716},{"id":"https://openalex.org/keywords/backdoor-mitigation","display_name":"backdoor mitigation","score":0.8370000123977661},{"id":"https://openalex.org/keywords/adversarial-robustness","display_name":"adversarial robustness","score":0.7229999899864197},{"id":"https://openalex.org/keywords/alzheimer-s-disease-diagnosis","display_name":"Alzheimer's disease diagnosis","score":0.7120000123977661},{"id":"https://openalex.org/keywords/mild-cognitive-impairment","display_name":"mild cognitive impairment","score":0.9850000143051147},{"id":"https://openalex.org/keywords/structural-mri","display_name":"structural MRI","score":0.8059999942779541},{"id":"https://openalex.org/keywords/neuron-pruning","display_name":"neuron pruning","score":0.6439999938011169}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.569100022315979},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.49079999327659607},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48809999227523804},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4821999967098236},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4244000017642975},{"id":"https://openalex.org/C2781045450","wikidata":"https://www.wikidata.org/wiki/Q254569","display_name":"Backdoor","level":2,"score":0.40779998898506165},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.3790999948978424},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.3513999879360199},{"id":"https://openalex.org/C95922358","wikidata":"https://www.wikidata.org/wiki/Q5432725","display_name":"False positive rate","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.33090001344680786},{"id":"https://openalex.org/C2984915365","wikidata":"https://www.wikidata.org/wiki/Q5141203","display_name":"Cognitive impairment","level":3,"score":0.31949999928474426},{"id":"https://openalex.org/C2779483572","wikidata":"https://www.wikidata.org/wiki/Q83030","display_name":"Dementia","level":3,"score":0.30489999055862427},{"id":"https://openalex.org/C58693492","wikidata":"https://www.wikidata.org/wiki/Q551875","display_name":"Neuroimaging","level":2,"score":0.3043999969959259},{"id":"https://openalex.org/C2778615406","wikidata":"https://www.wikidata.org/wiki/Q11072","display_name":"Amnesia","level":2,"score":0.3003999888896942},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2955000102519989},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.2612999975681305},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25589999556541443}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/brainsci16111191","doi":"https://doi.org/10.3390/brainsci16111191","is_oa":true,"landing_page_url":"https://doi.org/10.3390/brainsci16111191","pdf_url":null,"source":{"id":"https://openalex.org/S122317144","display_name":"Brain Sciences","issn_l":"2076-3425","issn":["2076-3425"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-scientific","cwts-core","doaj","norway-1","poland-100","russia-white-list-3","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Brain Sciences","raw_type":"journal-article"},{"id":"doi:10.20944/preprints202609.1729.v1","doi":"https://doi.org/10.20944/preprints202609.1729.v1","is_oa":true,"landing_page_url":"https://doi.org/10.20944/preprints202609.1729.v1","pdf_url":null,"source":{"id":"https://openalex.org/S6309402219","display_name":"Preprints.org","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.3390/brainsci16111191","doi":"https://doi.org/10.3390/brainsci16111191","is_oa":true,"landing_page_url":"https://doi.org/10.3390/brainsci16111191","pdf_url":null,"source":{"id":"https://openalex.org/S122317144","display_name":"Brain Sciences","issn_l":"2076-3425","issn":["2076-3425"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-scientific","cwts-core","doaj","norway-1","poland-100","russia-white-list-3","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Brain Sciences","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.8560000061988831,"id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322322","display_name":"King Abdulaziz University","ror":"https://ror.org/02ma4wv74"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4386433103","https://openalex.org/W7163378431","https://openalex.org/W7140312611","https://openalex.org/W4402288683","https://openalex.org/W7169791435","https://openalex.org/W4410887908","https://openalex.org/W7064181921","https://openalex.org/W3119388964","https://openalex.org/W7221438105","https://openalex.org/W4206730042"],"abstract_inverted_index":{"Background:":[0],"Deep":[1],"learning":[2],"classifiers":[3,101],"trained":[4],"on":[5,34,53,144,248],"structural":[6],"magnetic":[7],"resonance":[8],"imaging":[9],"(MRI)":[10],"are":[11],"increasingly":[12],"proposed":[13],"for":[14,73,261],"early":[15,262],"detection":[16,209,263],"of":[17,80,105,114,202,211,216],"Alzheimer\u2019s":[18],"disease":[19],"(AD)":[20],"and":[21,39,92,99,132,155,169,180,213,234],"mild":[22],"cognitive":[23,74,265],"impairment":[24],"(MCI),":[25],"yet":[26],"the":[27,106,126,142,185,203,222,257],"pipelines":[28],"that":[29,42,90,140,259],"produce":[30],"these":[31],"models":[32],"depend":[33],"shared":[35],"datasets,":[36],"pre-trained":[37],"weights,":[38],"federated":[40],"updates":[41],"an":[43,59,135],"adversary":[44],"can":[45],"poison.":[46],"A":[47,148],"backdoored":[48,218],"diagnostic":[49],"model":[50,127],"behaves":[51],"correctly":[52],"ordinary":[54],"scans":[55,162,250],"but":[56],"collapses":[57],"to":[58,159,192,253],"attacker-chosen":[60],"label":[61],"whenever":[62],"a":[63,70,77,87,170,207],"hidden":[64],"trigger":[65,119],"is":[66,256],"present,":[67],"which":[68,255],"turns":[69],"prognostic":[71],"instrument":[72],"aging":[75],"into":[76],"silent":[78],"source":[79],"misdiagnosis.":[81],"Methods:":[82],"This":[83],"article":[84],"proposes":[85],"MedBackdoorGuard,":[86],"defense":[88,224],"framework":[89,109,244],"detects":[91],"mitigates":[93],"backdoor":[94],"attacks":[95],"in":[96,121,264],"MRI-based":[97],"AD":[98],"MCI":[100,246],"without":[102],"prior":[103],"knowledge":[104],"trigger.":[107],"The":[108,243],"couples":[110],"spectral":[111],"latent":[112,118],"screening":[113],"training":[115],"data":[116],"with":[117,221],"isolation":[120],"activation":[122],"space,":[123],"then":[124],"immunizes":[125],"through":[128],"trigger-sensitive":[129],"neuron":[130],"pruning":[131],"fine-tuning":[133],"under":[134,176],"anatomical":[136],"attention":[137],"consistency":[138],"constraint":[139],"anchors":[141],"decision":[143],"medial":[145],"temporal":[146],"structures.":[147],"runtime":[149],"inference":[150],"guard":[151],"adds":[152],"superimposition":[153],"entropy":[154],"anatomically":[156],"informed":[157],"reconstruction":[158],"purify":[160],"suspicious":[161],"at":[163],"deployment.":[164],"Results:":[165],"Across":[166],"ADNI,":[167],"OASIS-1,":[168],"public":[171],"four-class":[172],"dementia":[173],"MRI":[174],"benchmark":[175],"patch,":[177],"blended,":[178],"warping,":[179],"frequency":[181],"triggers,":[182],"MedBackdoorGuard":[183],"reduces":[184],"mean":[186],"attack":[187,226],"success":[188,227],"rate":[189,228],"from":[190,251],"96.4%":[191],"3.0%":[193],"while":[194],"holding":[195],"clean":[196,235],"accuracy":[197,236],"within":[198],"0.6":[199],"percentage":[200,232,240],"points":[201,233],"undefended":[204],"model,":[205],"reaches":[206],"poisoned-sample":[208],"AUROC":[210],"97.4%,":[212],"identifies":[214],"59":[215],"60":[217],"models.":[219],"Compared":[220],"strongest":[223],"baseline,":[225],"falls":[229],"by":[230,238],"15.1":[231],"rises":[237],"1.7":[239],"points.":[241],"Conclusions:":[242],"restores":[245],"recall":[247],"triggered":[249],"3.9%":[252],"88.3%,":[254],"outcome":[258],"matters":[260],"aging.":[266]},"counts_by_year":[],"updated_date":"2026-11-03T10:50:19.383937","created_date":"2026-09-21T00:00:00"}
