{"id":"https://openalex.org/W7135083446","doi":"https://doi.org/10.48550/arxiv.2603.10526","title":"Sparse Task Vector Mixup with Hypernetworks for Efficient Knowledge Transfer in Whole-Slide Image Prognosis","display_name":"Sparse Task Vector Mixup with Hypernetworks for Efficient Knowledge Transfer in Whole-Slide Image Prognosis","publication_year":2026,"publication_date":"2026-03-11","ids":{"openalex":"https://openalex.org/W7135083446","doi":"https://doi.org/10.48550/arxiv.2603.10526"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.10526","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.10526","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.10526","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128806317","display_name":"Pei Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Liu, Pei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128896977","display_name":"Xiangxiang Zeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Xiangxiang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057420442","display_name":"Tengfei Ma","orcid":"https://orcid.org/0000-0002-1560-2261"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Tengfei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128860161","display_name":"Yucheng Xing","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xing, Yucheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109421487","display_name":"Xuanbai Ren","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren, Xuanbai","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128895489","display_name":"Yiping Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yiping","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5128806317"],"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":0.7587000131607056,"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":0.7587000131607056,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.04410000145435333,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.031300000846385956,"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/task","display_name":"Task (project management)","score":0.6542999744415283},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6432999968528748},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.609000027179718},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.5091999769210815},{"id":"https://openalex.org/keywords/knowledge-transfer","display_name":"Knowledge transfer","score":0.4011000096797943},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.3862999975681305},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3700000047683716},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.365200012922287},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.33169999718666077}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7095999717712402},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6632000207901001},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6542999744415283},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6432999968528748},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6395999789237976},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.609000027179718},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.5091999769210815},{"id":"https://openalex.org/C2776960227","wikidata":"https://www.wikidata.org/wiki/Q2586354","display_name":"Knowledge transfer","level":2,"score":0.4011000096797943},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.3862999975681305},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3700000047683716},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.365200012922287},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.33169999718666077},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3314000070095062},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.31349998712539673},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.27000001072883606},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.25529998540878296},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.10526","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.10526","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.10526","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.10526","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"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":[3,30],"widely":[4],"used":[5],"for":[6,26,116,179],"estimating":[7],"the":[8,22,36,48,117],"prognosis":[9],"of":[10],"cancer":[11,28,152],"patients.":[12],"Current":[13],"studies":[14],"generally":[15],"follow":[16],"a":[17,95,175],"cancer-specific":[18,159],"learning":[19,58,160,180],"paradigm.":[20],"However,":[21],"available":[23,198],"training":[24,76,190],"samples":[25,50],"one":[27],"type":[29],"usually":[31],"scarce":[32],"in":[33,86],"pathology.":[34],"Consequently,":[35],"model":[37,120],"often":[38],"struggles":[39],"to":[40,67,127,139],"learn":[41],"generalizable":[42,111],"knowledge,":[43],"thus":[44],"performing":[45],"worse":[46],"on":[47,73,150],"tumor":[49],"with":[51,102],"inherent":[52],"high":[53],"heterogeneity.":[54],"Although":[55],"multi-cancer":[56],"joint":[57,75,189],"and":[59,131,161,169],"knowledge":[60,112,164,182],"transfer":[61,165],"approaches":[62],"have":[63],"been":[64],"explored":[65],"recently":[66],"address":[68],"it,":[69],"they":[70],"either":[71],"rely":[72],"large-scale":[74,188],"or":[77,191],"extensive":[78,192],"inference":[79],"across":[80],"multiple":[81],"models,":[82],"posing":[83],"new":[84,96],"challenges":[85],"computational":[87],"efficiency.":[88],"To":[89],"this":[90,92],"end,":[91],"paper":[93],"proposes":[94],"scheme,":[97],"Sparse":[98],"Task":[99],"Vector":[100],"Mixup":[101],"Hypernetworks":[103],"(STEPH).":[104],"Unlike":[105],"previous":[106],"ones,":[107],"it":[108,173],"efficiently":[109],"absorbs":[110],"from":[113,183],"other":[114,184],"cancers":[115],"target":[118,143],"via":[119],"merging:":[121],"i)":[122],"applying":[123],"task":[124,136],"vector":[125,137],"mixup":[126],"each":[128],"source-target":[129],"pair":[130],"then":[132],"ii)":[133],"sparsely":[134],"aggregating":[135],"mixtures":[138],"obtain":[140],"an":[141,162],"improved":[142],"model,":[144],"driven":[145],"by":[146,167],"hypernetworks.":[147],"Extensive":[148],"experiments":[149],"13":[151],"datasets":[153],"show":[154],"that":[155],"STEPH":[156],"improves":[157],"over":[158],"existing":[163],"baseline":[166],"5.14%":[168],"2.01%,":[170],"respectively.":[171],"Moreover,":[172],"is":[174,196],"more":[176],"efficient":[177],"solution":[178],"prognostic":[181],"cancers,":[185],"without":[186],"requiring":[187],"multi-model":[193],"inference.":[194],"Code":[195],"publicly":[197],"at":[199],"https://github.com/liupei101/STEPH.":[200]},"counts_by_year":[],"updated_date":"2026-03-13T14:25:03.468858","created_date":"2026-03-13T00:00:00"}
