{"id":"https://openalex.org/W7164307443","doi":"https://doi.org/10.48550/arxiv.2606.11846","title":"SheafStain: Sheaf-Theoretic Schr\u00f6dinger Bridge for Spatially and Biologically Coherent Virtual Staining","display_name":"SheafStain: Sheaf-Theoretic Schr\u00f6dinger Bridge for Spatially and Biologically Coherent Virtual Staining","publication_year":2026,"publication_date":"2026-06-10","ids":{"openalex":"https://openalex.org/W7164307443","doi":"https://doi.org/10.48550/arxiv.2606.11846"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.11846","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11846","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":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.2606.11846","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128217748","display_name":"Hyeongyeol Lim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lim, Hyeongyeol","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138412673","display_name":"Hongjun Yoon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yoon, Hongjun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138476563","display_name":"Eunjin Jang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jang, Eunjin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128166582","display_name":"Daeky Jeong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jeong, Daeky","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138458474","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":"last","author":{"id":"https://openalex.org/A5051519943","display_name":"HwaMin Lee\ufffd","orcid":"https://orcid.org/0000-0002-6482-3511"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Hwamin","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":0.560699999332428,"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":0.560699999332428,"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.3237999975681305,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.023900000378489494,"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/image-stitching","display_name":"Image stitching","score":0.7257000207901001},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.6071000099182129},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.576200008392334},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5407999753952026},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49230000376701355},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4399999976158142},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.3855000138282776},{"id":"https://openalex.org/keywords/fragment","display_name":"Fragment (logic)","score":0.35690000653266907},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3418999910354614}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7764999866485596},{"id":"https://openalex.org/C29081049","wikidata":"https://www.wikidata.org/wiki/Q1364242","display_name":"Image stitching","level":2,"score":0.7257000207901001},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.6071000099182129},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.576200008392334},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5680999755859375},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5407999753952026},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49230000376701355},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4666000008583069},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4399999976158142},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3855000138282776},{"id":"https://openalex.org/C2776235265","wikidata":"https://www.wikidata.org/wiki/Q18392052","display_name":"Fragment (logic)","level":2,"score":0.35690000653266907},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3418999910354614},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.33730000257492065},{"id":"https://openalex.org/C194969405","wikidata":"https://www.wikidata.org/wiki/Q170519","display_name":"Virtual reality","level":2,"score":0.3294000029563904},{"id":"https://openalex.org/C108710211","wikidata":"https://www.wikidata.org/wiki/Q11538","display_name":"Mathematical proof","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C154504017","wikidata":"https://www.wikidata.org/wiki/Q853614","display_name":"Identifier","level":2,"score":0.31150001287460327},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.3000999987125397},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.28189998865127563},{"id":"https://openalex.org/C2779808786","wikidata":"https://www.wikidata.org/wiki/Q6664603","display_name":"Locality","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C2781294515","wikidata":"https://www.wikidata.org/wiki/Q2733470","display_name":"Stain","level":3,"score":0.2635999917984009},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2531999945640564},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.25220000743865967},{"id":"https://openalex.org/C3020199158","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"High resolution","level":2,"score":0.25209999084472656},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.11846","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11846","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":"doi:10.48550/arxiv.2606.11846","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11846","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":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":{"Current":[0],"virtual":[1,108],"staining":[2],"approaches":[3],"offer":[4,47],"the":[5,61,84,126,185,200],"potential":[6],"for":[7,21,60,103],"time-":[8],"and":[9,16,67,105,114,147,164,180,197,210],"cost-efficient":[10],"biomarker":[11],"quantification":[12],"in":[13],"cancer":[14],"diagnostics":[15],"prognostics.":[17],"However,":[18],"patch-wise":[19],"inference":[20],"gigapixel":[22],"whole":[23],"slide":[24],"images":[25],"(WSIs)":[26],"fails":[27],"to":[28,56],"maintain":[29],"spatial":[30,137],"continuity,":[31],"yielding":[32],"artifacts":[33],"that":[34,82,96,172],"cause":[35],"catastrophic":[36],"mismatches":[37],"with":[38],"ground-truth":[39],"images.":[40],"Although":[41],"pathology":[42],"Vision":[43],"Foundation":[44],"Models":[45],"(VFMs)":[46],"rich":[48],"representations,":[49],"their":[50],"self-attention":[51],"causes":[52],"varying":[53],"global":[54],"contexts":[55],"produce":[57],"inconsistent":[58],"embeddings":[59,78],"same":[62],"physical":[63],"region.":[64],"We":[65],"formalize":[66],"validate":[68],"this":[69],"``context":[70],"contamination''":[71],"as":[72,100,122],"a":[73,80,93,118,135,155],"sheaf-theoretic":[74],"problem":[75],"where":[76],"these":[77],"form":[79,134],"presheaf":[81],"violates":[83],"gluing":[85],"axiom.":[86],"To":[87],"address":[88],"this,":[89],"we":[90,191],"propose":[91],"SheafStain,":[92],"new":[94],"approach":[95],"reinterprets":[97],"VFM":[98,157],"features":[99],"sheaf-like":[101,123],"sections":[102],"spatially":[104],"biologically":[106],"coherent":[107],"staining.":[109],"Specifically,":[110],"SheafStain":[111,212],"integrates":[112],"class":[113,127],"patch":[115,132],"tokens":[116,133],"into":[117],"Schr\u00f6dinger":[119],"Bridge":[120],"framework":[121],"sections.":[124],"While":[125],"token":[128],"anchors":[129],"biological":[130],"consistency,":[131],"per-position":[136],"map.":[138],"A":[139],"backbone":[140],"co-pretrained":[141],"on":[142,174,199],"Hematoxylin":[143],"\\&":[144],"Eosin":[145],"(H\\&E)":[146],"Immunohistochemistry":[148],"(IHC)":[149],"yields":[150],"non-degenerate":[151],"cross-stain":[152],"stalks,":[153],"so":[154],"single":[156],"feature":[158],"space":[159],"supervises":[160],"both":[161],"input":[162],"conditioning":[163],"output":[165],"stain":[166],"alignment.":[167],"Departing":[168],"from":[169],"prior":[170,218],"work":[171],"evaluates":[173],"isolated":[175],"$256":[176,194],"\\times":[177,187,195,203],"256$":[178,196],"patches":[179],"either":[181],"random-crops":[182],"or":[183],"resizes":[184],"$1024":[186,202],"1024$":[188,204],"ground":[189],"truth,":[190],"translate":[192],"at":[193],"evaluate":[198],"stitched":[201],"outputs":[205],"across":[206],"HER2,":[207],"ER,":[208],"PR,":[209],"Ki-67.":[211],"demonstrates":[213],"promising":[214],"results":[215],"against":[216],"six":[217],"methods":[219],"while":[220],"mitigating":[221],"patch-boundary":[222],"stitching":[223],"artifacts.":[224],"Code":[225],"will":[226],"soon":[227],"be":[228],"released.":[229]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-12T00:00:00"}