{"id":"https://openalex.org/W7162139241","doi":"https://doi.org/10.48550/arxiv.2605.22249","title":"D3Seg: Dependency-Aware Diffusion for Brain Tumor Segmentation with Missing Modalities","display_name":"D3Seg: Dependency-Aware Diffusion for Brain Tumor Segmentation with Missing Modalities","publication_year":2026,"publication_date":"2026-05-21","ids":{"openalex":"https://openalex.org/W7162139241","doi":"https://doi.org/10.48550/arxiv.2605.22249"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.22249","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22249","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.2605.22249","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136767887","display_name":"Danish Ali","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ali, Danish","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136735188","display_name":"Ajmal Mian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mian, Ajmal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136799306","display_name":"Naveed Akhtar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Akhtar, Naveed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5023496317","display_name":"Ghulam Mubashar Hassan","orcid":"https://orcid.org/0000-0002-6636-8807"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hassan, Ghulam Mubashar","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/T10036","display_name":"Advanced Neural Network Applications","score":0.6444000005722046,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.6444000005722046,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.20759999752044678,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.04809999838471413,"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/segmentation","display_name":"Segmentation","score":0.6635000109672546},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.5342000126838684},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.5192999839782715},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4878999888896942},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.37450000643730164},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.35850000381469727},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.3553999960422516},{"id":"https://openalex.org/keywords/voxel","display_name":"Voxel","score":0.3310999870300293}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6682999730110168},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6635000109672546},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6599000096321106},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.5342000126838684},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.5192999839782715},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4878999888896942},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.37450000643730164},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.35850000381469727},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.3553999960422516},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.3310999870300293},{"id":"https://openalex.org/C149550507","wikidata":"https://www.wikidata.org/wiki/Q899360","display_name":"Diffusion MRI","level":3,"score":0.32499998807907104},{"id":"https://openalex.org/C87619178","wikidata":"https://www.wikidata.org/wiki/Q126002","display_name":"Concatenation (mathematics)","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.31299999356269836},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C2910607126","wikidata":"https://www.wikidata.org/wiki/Q48790829","display_name":"Multiparametric MRI","level":4,"score":0.2727999985218048},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.257999986410141}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.22249","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22249","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.2605.22249","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22249","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":{"Accurate":[0],"brain":[1],"tumor":[2,116,150,174,180],"segmentation":[3,40,61],"using":[4],"multi-parametric":[5],"MRI":[6,21,31],"is":[7,23,65],"critical":[8],"for":[9,93],"effective":[10],"treatment":[11],"planning.":[12],"However,":[13],"in":[14,34,38,100,213],"clinical":[15],"settings,":[16],"complete":[17],"acquisition":[18],"of":[19,29,114,139,206],"all":[20],"sequences":[22],"not":[24],"always":[25],"possible.":[26],"The":[27,152,164],"absence":[28],"certain":[30],"modalities":[32],"results":[33,153],"substantial":[35],"performance":[36,70],"degradation":[37],"existing":[39],"methods,":[41],"which":[42,64],"typically":[43],"rely":[44],"on":[45,124,136,172,179,193,199],"naive":[46],"feature":[47,98],"concatenation":[48],"or":[49],"direct":[50],"fusion":[51],"strategies.":[52],"To":[53],"address":[54],"this":[55],"limitation,":[56],"we":[57],"propose":[58],"a":[59,86,137],"novel":[60],"model":[62,82,123,166,192],"D3Seg":[63,74,122],"designed":[66],"to":[67,81,91,107,146,188],"maintain":[68],"stable":[69],"under":[71,160],"missing-modality":[72,162,185,229],"settings.":[73],"introduces":[75],"Multi-hop":[76],"Modality":[77],"Graph":[78],"Fusion":[79],"(MMGF)":[80],"higher-order":[83],"inter-modality":[84],"dependencies,":[85],"lightweight":[87],"diffusion-based":[88],"imputation":[89],"mechanism":[90],"compensate":[92],"missing":[94],"T1ce":[95],"and":[96,103,111,132,176,217,223],"FLAIR":[97],"representations":[99],"latent":[101],"space,":[102],"probability-space":[104],"decision":[105],"refinement":[106],"mitigate":[108],"dominant-class":[109],"overconfidence":[110],"improve":[112],"delineation":[113],"underrepresented":[115],"subregions.":[117],"We":[118],"evaluate":[119],"the":[120,129,140,157,189,204,207,214],"proposed":[121,165,208],"BraTS":[125,142,194,200],"2023":[126,143],"Glioma":[127,195],"as":[128],"primary":[130],"benchmark":[131],"further":[133],"test":[134],"it":[135],"subset":[138],"external":[141],"Meningioma":[144,201],"cohort":[145],"assess":[147],"generalization":[148],"across":[149,183,227],"pathologies.":[151],"are":[154],"compared":[155,187],"with":[156,220],"state-of-the-art":[158,191],"models":[159],"different":[161],"conditions.":[163],"achieves":[167],"approximately":[168,221],"1.5-2.0%":[169],"Dice":[170],"improvement":[171],"enhancing":[173],"(ET)":[175],"around":[177],"1.0%":[178],"core":[181],"(TC)":[182],"multiple":[184],"configurations":[186],"current":[190],"dataset.":[196],"Cross-cohort":[197],"evaluation":[198],"dataset":[202],"demonstrates":[203],"generalizability":[205],"model,":[209],"showing":[210],"consistent":[211],"improvements":[212],"challenging":[215],"TC":[216],"ET":[218],"regions,":[219],"1.5-3.0%":[222],"1.5-6.5%":[224],"gains":[225],"respectively":[226],"several":[228],"configurations.":[230]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-23T00:00:00"}