{"id":"https://openalex.org/W7165385234","doi":"https://doi.org/10.48550/arxiv.2606.19966","title":"Semantic-Anchored Evidential Fusion for Domain-Robust Whole-Slide Survival Analysis","display_name":"Semantic-Anchored Evidential Fusion for Domain-Robust Whole-Slide Survival Analysis","publication_year":2026,"publication_date":"2026-06-18","ids":{"openalex":"https://openalex.org/W7165385234","doi":"https://doi.org/10.48550/arxiv.2606.19966"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.19966","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19966","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2606.19966","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139010137","display_name":"Yucheng Xing","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xing, Yucheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139010384","display_name":"Ling Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Ling","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138967934","display_name":"Pei Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Pei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085158197","display_name":"J Y","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Jingying","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139016150","display_name":"Jiaqing Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Jiaqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138975485","display_name":"Kai He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Kai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138968964","display_name":"Mengling Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Mengling","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/T10862","display_name":"AI in cancer detection","score":0.5713000297546387,"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/T10862","display_name":"AI in cancer detection","score":0.5713000297546387,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.14880000054836273,"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":0.06459999829530716,"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/robustness","display_name":"Robustness (evolution)","score":0.6148999929428101},{"id":"https://openalex.org/keywords/evidential-reasoning-approach","display_name":"Evidential reasoning approach","score":0.4902999997138977},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4169999957084656},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4059000015258789},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3869999945163727},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.38690000772476196},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.374099999666214}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7240999937057495},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.670799970626831},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6148999929428101},{"id":"https://openalex.org/C156201811","wikidata":"https://www.wikidata.org/wiki/Q5418360","display_name":"Evidential reasoning approach","level":4,"score":0.4902999997138977},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4772999882698059},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4169999957084656},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4059000015258789},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3968000113964081},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3869999945163727},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.38690000772476196},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.374099999666214},{"id":"https://openalex.org/C2777946921","wikidata":"https://www.wikidata.org/wiki/Q7449044","display_name":"Semantic analysis (machine learning)","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C173414695","wikidata":"https://www.wikidata.org/wiki/Q5510276","display_name":"Fusion mechanism","level":4,"score":0.2969000041484833},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.29109999537467957},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2806999981403351},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2741999924182892},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.25290000438690186}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.19966","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19966","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2606.19966","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19966","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Whole-slide":[0],"images":[1],"(WSIs)":[2],"are":[3,36],"widely":[4],"used":[5],"for":[6,179],"computational":[7],"cancer":[8],"prognosis.":[9],"However,":[10],"most":[11],"existing":[12],"methods":[13],"primarily":[14],"focus":[15],"on":[16,32,132],"in-domain":[17],"performance":[18],"and":[19,46,59,112,115,136,152],"fail":[20],"to":[21,39,109,123],"generalize":[22],"across":[23,139],"clinical":[24,181],"centers.":[25],"This":[26],"limitation":[27],"stems":[28],"from":[29,91,127],"their":[30,177],"reliance":[31],"pixel-derived":[33,174],"representations":[34],"that":[35,51,67,164],"highly":[37],"susceptible":[38],"domain-specific":[40],"artifacts":[41],"caused":[42],"by":[43,158],"staining":[44],"protocols":[45],"scanner":[47],"hardware.":[48],"We":[49],"hypothesize":[50],"high-level":[52],"pathology":[53],"semantics,":[54],"such":[55],"as":[56],"tumor":[57],"grade":[58],"micro-environmental":[60],"architecture,":[61,104],"provide":[62],"a":[63,79,99,119],"domain-invariant":[64],"semantic":[65,89,114,166],"representation":[66],"mirrors":[68],"the":[69,155],"robust":[70],"diagnostic":[71],"logic":[72],"of":[73],"human":[74],"pathologists.":[75],"Therefore,":[76],"we":[77],"propose":[78],"Semantic-Anchored":[80],"Evidential":[81],"Fusion":[82],"Survival":[83],"(SAEFS)":[84],"framework,":[85],"where":[86],"SAEFS":[87,143],"derives":[88],"anchors":[90],"WSIs":[92],"via":[93],"Visual":[94],"Question":[95],"Answering":[96],"(VQA),":[97],"employs":[98],"dual-stream":[100],"WSI":[101],"evidence":[102,117],"extraction":[103],"uses":[105],"Dirichlet-based":[106],"Subjective":[107],"Logic":[108],"model":[110],"uncertainty,":[111],"fuses":[113],"visual":[116],"through":[118],"cautious":[120],"conjunction":[121],"rule":[122],"avoid":[124],"overconfident":[125],"fusion":[126],"correlated":[128],"sources.":[129],"Trained":[130],"exclusively":[131],"one":[133],"source":[134],"domain":[135],"evaluated":[137],"zero-shot":[138],"four":[140],"unseen":[141],"domains,":[142],"consistently":[144],"outperforms":[145],"state-of-the-art":[146],"models":[147],"both":[148],"in":[149],"prediction":[150],"accuracy":[151],"reliability,":[153],"improving":[154],"average":[156],"C-index":[157],"10.2%.":[159],"Quantitative":[160],"analyses":[161],"further":[162],"show":[163],"VQA-derived":[165],"features":[167],"exhibit":[168],"significantly":[169],"lower":[170],"cross-center":[171,180],"divergence":[172],"than":[173],"features,":[175],"highlighting":[176],"robustness":[178],"applications.":[182]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-20T00:00:00"}
