{"id":"https://openalex.org/W7166712315","doi":"https://doi.org/10.48550/arxiv.2606.28697","title":"Mitigating Batch Effects in Histopathology via Language-Mediated Robust Embedding Generation","display_name":"Mitigating Batch Effects in Histopathology via Language-Mediated Robust Embedding Generation","publication_year":2026,"publication_date":"2026-06-27","ids":{"openalex":"https://openalex.org/W7166712315","doi":"https://doi.org/10.48550/arxiv.2606.28697"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.28697","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28697","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.28697","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139652875","display_name":"Yishu Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yishu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068730978","display_name":"Shushan Wu","orcid":"https://orcid.org/0000-0001-7594-0273"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Shushan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139698528","display_name":"Zhenzhong Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhenzhong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139700363","display_name":"Didong Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Didong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139632889","display_name":"Huaxiu Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Huaxiu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139683105","display_name":"Yun Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137838474","display_name":"Iain Carmichael","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Carmichael, Iain","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002773659","display_name":"Katherine A. Hoadley","orcid":"https://orcid.org/0000-0002-1216-477X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hoadley, Katherine A.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058268359","display_name":"Hao Zhu","orcid":"https://orcid.org/0000-0003-4411-0933"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Hongtu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106974408","display_name":"Di Wu","orcid":"https://orcid.org/0000-0003-2169-8236"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Di","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139691297","display_name":"Daiwei Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Daiwei","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":0.9564999938011169,"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":0.9564999938011169,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.011800000444054604,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.003700000001117587,"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/embedding","display_name":"Embedding","score":0.7419999837875366},{"id":"https://openalex.org/keywords/digital-pathology","display_name":"Digital pathology","score":0.631600022315979},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6236000061035156},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6003000140190125},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44290000200271606},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3504999876022339}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7419999837875366},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.710099995136261},{"id":"https://openalex.org/C2777522853","wikidata":"https://www.wikidata.org/wiki/Q5276128","display_name":"Digital pathology","level":2,"score":0.631600022315979},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6236000061035156},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6003000140190125},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5993000268936157},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44290000200271606},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3630000054836273},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3504999876022339},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.34119999408721924},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.33070001006126404},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C2781294515","wikidata":"https://www.wikidata.org/wiki/Q2733470","display_name":"Stain","level":3,"score":0.3131999969482422},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.265500009059906}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.28697","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28697","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.28697","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28697","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":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5253282785415649,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Pathology":[0,60],"foundation":[1],"models":[2,86],"(PFMs)":[3],"have":[4],"demonstrated":[5],"strong":[6],"potential":[7,136],"across":[8,26],"clinical":[9],"and":[10,36,88,144,153],"scientific":[11],"applications,":[12],"yet":[13],"their":[14],"performance":[15],"is":[16,108],"often":[17],"hindered":[18],"by":[19],"batch":[20],"effects,":[21],"which":[22],"are":[23],"non-biological":[24],"variations":[25],"tissue":[27],"source":[28],"institutions":[29],"(TSIs)":[30],"that":[31,65],"distort":[32],"learned":[33],"feature":[34],"representations":[35],"impair":[37],"generalization.":[38,103],"Conventional":[39],"mitigation":[40],"strategies,":[41],"such":[42],"as":[43,120],"stain":[44],"normalization,":[45],"offer":[46],"limited":[47],"success":[48],"in":[49,141],"addressing":[50],"these":[51],"high-dimensional,":[52],"complex":[53],"artifacts.":[54],"We":[55],"present":[56],"GLMP":[57,91,107],"(General-purpose":[58],"LLM-Mediated":[59],"model),":[61],"a":[62,146],"novel":[63],"framework":[64],"generates":[66],"robust":[67,154],"numerical":[68,126],"embeddings":[69,127],"from":[70,128],"histology":[71,129],"image":[72],"patches":[73],"through":[74],"an":[75,121],"intermediate":[76,122],"textual":[77],"representation.":[78],"By":[79],"leveraging":[80],"pretrained":[81],"general-purpose":[82],"multimodal":[83],"large":[84],"language":[85],"(MLLMs)":[87],"text":[89,115],"encoders,":[90],"effectively":[92],"prioritizes":[93],"biologically":[94],"meaningful":[95],"signals":[96],"over":[97],"TSI-specific":[98],"artifacts,":[99],"thereby":[100],"improving":[101],"cross-institutional":[102],"To":[104],"our":[105],"knowledge,":[106],"the":[109,134],"first":[110],"pathology":[111,143,155],"model":[112],"to":[113],"use":[114],"descriptions":[116],"of":[117,137],"histological":[118],"features":[119],"representation":[123],"for":[124,149],"generating":[125],"images.":[130],"Our":[131],"results":[132],"highlight":[133],"untapped":[135],"broad-domain,":[138],"non-specialized":[139],"MLLMs":[140],"computational":[142],"introduce":[145],"new":[147],"paradigm":[148],"building":[150],"versatile,":[151],"generalizable,":[152],"models.":[156]},"counts_by_year":[],"updated_date":"2026-07-01T06:29:00.853634","created_date":"2026-07-01T00:00:00"}
