{"id":"https://openalex.org/W7137200316","doi":"https://doi.org/10.48550/arxiv.2603.12886","title":"A protocol for evaluating robustness to H&E staining variation in computational pathology models","display_name":"A protocol for evaluating robustness to H&E staining variation in computational pathology models","publication_year":2026,"publication_date":"2026-03-13","ids":{"openalex":"https://openalex.org/W7137200316","doi":"https://doi.org/10.48550/arxiv.2603.12886"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.12886","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12886","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2603.12886","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129450134","display_name":"Lydia A. Sch\u00f6npflug","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Sch\u00f6npflug, Lydia A.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129618349","display_name":"Nikki van den Berg","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Berg, Nikki van den","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023834622","display_name":"Sonali Andani","orcid":"https://orcid.org/0000-0003-3251-5108"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andani, Sonali","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078112704","display_name":"Nanda Horeweg","orcid":"https://orcid.org/0000-0002-8581-4753"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Horeweg, Nanda","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021849860","display_name":"Jurriaan Barkey Wolf","orcid":"https://orcid.org/0000-0002-7811-0280"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wolf, Jurriaan Barkey","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025042206","display_name":"Tjalling Bosse","orcid":"https://orcid.org/0000-0002-6881-8437"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bosse, Tjalling","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129486196","display_name":"Viktor H. Koelzer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Koelzer, Viktor H.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5077058928","display_name":"Maxime W. Lafarge","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lafarge, Maxime W.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5129450134"],"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":0.9513000249862671,"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":0.9513000249862671,"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":0.01080000028014183,"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/T10885","display_name":"Gene expression and cancer classification","score":0.00419999985024333,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7824000120162964},{"id":"https://openalex.org/keywords/staining","display_name":"Staining","score":0.7224000096321106},{"id":"https://openalex.org/keywords/h-e-stain","display_name":"H&E stain","score":0.45669999718666077},{"id":"https://openalex.org/keywords/protocol","display_name":"Protocol (science)","score":0.39160001277923584},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39010000228881836},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.35249999165534973}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7824000120162964},{"id":"https://openalex.org/C74864618","wikidata":"https://www.wikidata.org/wiki/Q2332446","display_name":"Staining","level":2,"score":0.7224000096321106},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5817000269889832},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5133000016212463},{"id":"https://openalex.org/C125473707","wikidata":"https://www.wikidata.org/wiki/Q9914","display_name":"H&E stain","level":3,"score":0.45669999718666077},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.39160001277923584},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39010000228881836},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.35249999165534973},{"id":"https://openalex.org/C2781294515","wikidata":"https://www.wikidata.org/wiki/Q2733470","display_name":"Stain","level":3,"score":0.34060001373291016},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.29750001430511475},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2906999886035919},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2856000065803528},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C2779714256","wikidata":"https://www.wikidata.org/wiki/Q25305062","display_name":"Multiple Models","level":2,"score":0.25609999895095825}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.12886","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12886","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.12886","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12886","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"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":{"Sensitivity":[0],"to":[1,8,44,97],"staining":[2,19,46,55,62,72,81,158,170,224],"variation":[3,47],"remains":[4],"a":[5,38,78,190],"major":[6],"barrier":[7],"deploying":[9],"computational":[10],"pathology":[11],"(CPath)":[12],"models":[13,108,123,168],"as":[14,146,150],"hematoxylin":[15],"and":[16,148,169,188,216],"eosin":[17],"(H&E)":[18],"varies":[20],"across":[21,129,155,222],"laboratories,":[22],"requiring":[23],"systematic":[24],"assessment":[25],"of":[26,102,229],"how":[27],"this":[28,34],"variability":[29],"affects":[30],"model":[31,214,234],"prediction.":[32],"In":[33],"work,":[35],"we":[36,75,93,204],"developed":[37],"three-step":[39],"protocol":[40,96,210],"for":[41,232],"evaluating":[42],"robustness":[43,100,149],"H&E":[45,161,164,223],"in":[48],"CPath":[49,67,213],"models.":[50,141],"Step":[51,57,64],"1:":[52],"Select":[53],"reference":[54,71,80],"conditions,":[56,171,225],"2:":[58],"Characterize":[59],"test":[60],"set":[61],"properties,":[63],"3:":[65],"Apply":[66],"model(s)":[68],"under":[69],"simulated":[70,157],"conditions.":[73],"Here,":[74],"first":[76],"created":[77],"new":[79],"library":[82],"based":[83],"on":[84,109,125],"the":[85,95,99,110,126,151,207,227],"PLISM":[86],"dataset.":[87],"As":[88],"an":[89],"exemplary":[90],"use":[91],"case,":[92],"applied":[94],"assess":[98],"properties":[101],"306":[103],"microsatellite":[104],"instability":[105],"(MSI)":[106],"classification":[107,140,172,195],"unseen":[111],"SurGen":[112],"colorectal":[113],"cancer":[114],"dataset":[115],"(n=738),":[116],"including":[117],"300":[118],"attention-based":[119],"multiple":[120],"instance":[121],"learning":[122],"trained":[124],"TCGA-COAD/READ":[127],"datasets":[128],"three":[130],"feature":[131],"extractors":[132],"(UNI2-h,":[133],"H-Optimus-1,":[134],"Virchow2),":[135],"alongside":[136],"six":[137],"public":[138],"MSI":[139],"Classification":[142],"performance":[143,173,196,220],"was":[144],"measured":[145],"AUC,":[147],"min-max":[152],"AUC":[153,176],"range":[154],"four":[156],"conditions":[159],"(low/high":[160],"intensity,":[162],"low/high":[163],"color":[165],"similarity).":[166],"Across":[167],"ranged":[174,182],"from":[175,183],"0.769-0.911":[177],"($\u0394$":[178,185],"=":[179,186],"0.142).":[180],"Robustness":[181],"0.007-0.079":[184],"0.072),":[187],"showed":[189],"weak":[191],"inverse":[192],"correlation":[193],"with":[194],"(Pearson":[197],"r=-0.22,":[198],"95%":[199],"CI":[200],"[-0.34,":[201],"-0.11]).":[202],"Thus,":[203],"show":[205],"that":[206],"proposed":[208],"evaluation":[209],"enables":[211],"robustness-informed":[212],"selection":[215],"provides":[217],"insight":[218],"into":[219],"shifts":[221],"supporting":[226],"identification":[228],"operational":[230],"ranges":[231],"reliable":[233],"deployment.":[235],"Code":[236],"is":[237],"available":[238],"at":[239],"https://github.com/CTPLab/staining-robustness-evaluation":[240],".":[241]},"counts_by_year":[],"updated_date":"2026-05-04T08:30:34.212998","created_date":"2026-03-17T00:00:00"}