{"id":"https://openalex.org/W4416548851","doi":"https://doi.org/10.48550/arxiv.2508.00383","title":"$MV_{Hybrid}$: Improving Spatial Transcriptomics Prediction with Hybrid State Space-Vision Transformer Backbone in Pathology Vision Foundation Models","display_name":"$MV_{Hybrid}$: Improving Spatial Transcriptomics Prediction with Hybrid State Space-Vision Transformer Backbone in Pathology Vision Foundation Models","publication_year":2025,"publication_date":"2025-08-01","ids":{"openalex":"https://openalex.org/W4416548851","doi":"https://doi.org/10.48550/arxiv.2508.00383"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2508.00383","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.00383","pdf_url":"https://arxiv.org/pdf/2508.00383","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2508.00383","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112327081","display_name":"Won June Cho","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cho, Won June","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054049458","display_name":"Hong\u2010Jun Yoon","orcid":"https://orcid.org/0000-0002-5450-5878"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yoon, Hongjun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Jeong, Daeky","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jeong, Daeky","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Lim, Hyeongyeol","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lim, Hyeongyeol","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5060938154","display_name":"Yosep Chong","orcid":"https://orcid.org/0000-0001-8615-3064"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chong, Yosep","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":0.5388000011444092,"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":0.5388000011444092,"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/T11289","display_name":"Single-cell and spatial transcriptomics","score":0.15770000219345093,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.06350000202655792,"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/transformer","display_name":"Transformer","score":0.4999000132083893},{"id":"https://openalex.org/keywords/digital-pathology","display_name":"Digital pathology","score":0.47119998931884766},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41609999537467957},{"id":"https://openalex.org/keywords/transcriptome","display_name":"Transcriptome","score":0.36329999566078186},{"id":"https://openalex.org/keywords/transferability","display_name":"Transferability","score":0.3458000123500824},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.3431999981403351},{"id":"https://openalex.org/keywords/expression","display_name":"Expression (computer science)","score":0.3303000032901764},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.32839998602867126}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6557999849319458},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.569599986076355},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4999000132083893},{"id":"https://openalex.org/C2777522853","wikidata":"https://www.wikidata.org/wiki/Q5276128","display_name":"Digital pathology","level":2,"score":0.47119998931884766},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45829999446868896},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41609999537467957},{"id":"https://openalex.org/C162317418","wikidata":"https://www.wikidata.org/wiki/Q252857","display_name":"Transcriptome","level":4,"score":0.36329999566078186},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35420000553131104},{"id":"https://openalex.org/C61272859","wikidata":"https://www.wikidata.org/wiki/Q7834031","display_name":"Transferability","level":3,"score":0.3458000123500824},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.3431999981403351},{"id":"https://openalex.org/C90559484","wikidata":"https://www.wikidata.org/wiki/Q778379","display_name":"Expression (computer science)","level":2,"score":0.3303000032901764},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.32839998602867126},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C2779808786","wikidata":"https://www.wikidata.org/wiki/Q6664603","display_name":"Locality","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C2781197716","wikidata":"https://www.wikidata.org/wiki/Q864574","display_name":"Biomarker","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.28929999470710754},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C67339327","wikidata":"https://www.wikidata.org/wiki/Q1502576","display_name":"Gene regulatory network","level":4,"score":0.26489999890327454},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.262800008058548},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.26260000467300415},{"id":"https://openalex.org/C101722063","wikidata":"https://www.wikidata.org/wiki/Q218825","display_name":"Random access","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C18431079","wikidata":"https://www.wikidata.org/wiki/Q1502169","display_name":"Gene expression profiling","level":4,"score":0.25290000438690186}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2508.00383","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.00383","pdf_url":"https://arxiv.org/pdf/2508.00383","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2508.00383","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.00383","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2508.00383","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.00383","pdf_url":"https://arxiv.org/pdf/2508.00383","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"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":{"Spatial":[0],"transcriptomics":[1],"reveals":[2],"gene":[3,30,184],"expression":[4,31,185],"patterns":[5,86],"within":[6],"tissue":[7],"context,":[8],"enabling":[9],"precision":[10],"oncology":[11],"applications":[12],"such":[13],"as":[14,217],"treatment":[15],"response":[16],"prediction,":[17,186],"but":[18],"its":[19,215],"high":[20],"cost":[21],"and":[22,152,173,190,205],"technical":[23],"complexity":[24],"limit":[25],"clinical":[26,57],"adoption.":[27],"Predicting":[28],"spatial":[29],"(biomarkers)":[32],"from":[33],"routine":[34],"histopathology":[35],"images":[36],"offers":[37],"a":[38,109,218],"practical":[39],"alternative,":[40],"yet":[41],"current":[42],"vision":[43],"foundation":[44],"models":[45,96,116,147],"(VFMs)":[46],"in":[47,183,201],"pathology":[48,128,220],"based":[49],"on":[50,65,132],"Vision":[51],"Transformer":[52],"(ViT)":[53],"backbones":[54],"perform":[55],"below":[56],"standards.":[58],"Given":[59],"that":[60,74,93,211],"VFMs":[61],"are":[62],"already":[63],"trained":[64],"millions":[66],"of":[67,156,212],"diverse":[68],"whole":[69],"slide":[70],"images,":[71],"we":[72,106],"hypothesize":[73],"architectural":[75],"innovations":[76],"beyond":[77],"ViTs":[78],"may":[79],"better":[80,198],"capture":[81],"the":[82,138,157,170],"low-frequency,":[83],"subtle":[84],"morphological":[85],"correlating":[87],"with":[88,98,118],"molecular":[89],"phenotypes.":[90],"By":[91],"demonstrating":[92,187],"state":[94,114],"space":[95,115],"initialized":[97],"negative":[99],"real":[100],"eigenvalues":[101],"exhibit":[102],"strong":[103],"low-frequency":[104],"bias,":[105],"introduce":[107],"$MV_{Hybrid}$,":[108],"hybrid":[110],"backbone":[111,125],"architecture":[112],"combining":[113],"(SSMs)":[117],"ViT.":[119],"We":[120,143],"compare":[121],"five":[122],"other":[123],"different":[124],"architectures":[126],"for":[127],"VFMs,":[129],"all":[130,145],"pretrained":[131,146],"identical":[133],"colorectal":[134],"cancer":[135],"datasets":[136],"using":[137,148],"DINOv2":[139],"self-supervised":[140],"learning":[141],"method.":[142],"evaluate":[144],"both":[149],"random":[150,181],"split":[151,182],"leave-one-study-out":[153],"(LOSO)":[154],"settings":[155],"same":[158],"biomarker":[159],"dataset.":[160],"In":[161],"LOSO":[162],"evaluation,":[163],"$MV_{Hybrid}$":[164,194],"achieves":[165],"57%":[166],"higher":[167],"correlation":[168],"than":[169],"best-performing":[171],"ViT":[172],"shows":[174,195],"43%":[175],"smaller":[176],"performance":[177,189,200],"degradation":[178],"compared":[179,209],"to":[180,210],"superior":[188],"robustness,":[191],"respectively.":[192],"Furthermore,":[193],"equal":[196],"or":[197],"downstream":[199],"classification,":[202],"patch":[203],"retrieval,":[204],"survival":[206],"prediction":[207],"tasks":[208],"ViT,":[213],"showing":[214],"promise":[216],"next-generation":[219],"VFM":[221],"backbone.":[222],"Our":[223],"code":[224],"is":[225],"publicly":[226],"available":[227],"at:":[228],"https://github.com/deepnoid-ai/MVHybrid.":[229]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}
