{"id":"https://openalex.org/W7221032865","doi":"https://doi.org/10.64898/2026.10.01.755883","title":"Quantifying performance inflation from hidden sample dependence in biomedical image classification benchmarks","display_name":"Quantifying performance inflation from hidden sample dependence in biomedical image classification benchmarks","publication_year":2026,"publication_date":"2026-10-08","ids":{"openalex":"https://openalex.org/W7221032865","doi":"https://doi.org/10.64898/2026.10.01.755883"},"language":"en","primary_location":{"id":"doi:10.64898/2026.10.01.755883","doi":"https://doi.org/10.64898/2026.10.01.755883","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.10.01.755883","pdf_url":"https://www.biorxiv.org/content/biorxiv/early/2026/10/07/2026.10.01.755883.full.pdf","source":{"id":"https://openalex.org/S4306402567","display_name":"bioRxiv (Cold Spring Harbor Laboratory)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2750212522","host_organization_name":"Cold Spring Harbor Laboratory","host_organization_lineage":["https://openalex.org/I2750212522"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"bioRxiv","raw_type":"posted-content"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.biorxiv.org/content/biorxiv/early/2026/10/07/2026.10.01.755883.full.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127878433","display_name":"Luk\u00e1\u0161 Jochymek","orcid":"https://orcid.org/0009-0002-7243-2884","observed_orcids":["https://orcid.org/0009-0002-7243-2884"]},"institutions":[{"id":"https://openalex.org/I142208455","display_name":"VSB - Technical University of Ostrava","ror":"https://ror.org/05x8mcb75","country_code":"CZ","type":"education","lineage":["https://openalex.org/I142208455"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Luk\u00e1\u0161 Jochymek","raw_affiliation_strings":["Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB\u2013Technical University of Ostrava, Ostrava, Czech Republic"],"raw_orcid":"https://orcid.org/0009-0002-7243-2884","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB\u2013Technical University of Ostrava, Ostrava, Czech Republic","institution_ids":["https://openalex.org/I142208455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053887935","display_name":"Mark\u00e9ta Va\u0161inkov\u00e1","orcid":"https://orcid.org/0009-0003-9321-0751","observed_orcids":["https://orcid.org/0009-0003-9321-0751"]},"institutions":[{"id":"https://openalex.org/I142208455","display_name":"VSB - Technical University of Ostrava","ror":"https://ror.org/05x8mcb75","country_code":"CZ","type":"education","lineage":["https://openalex.org/I142208455"]}],"countries":["CZ"],"is_corresponding":true,"raw_author_name":"Mark\u00e9ta Va\u0161inkov\u00e1","raw_affiliation_strings":["Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB\u2013Technical University of Ostrava, Ostrava, Czech Republic"],"raw_orcid":"https://orcid.org/0009-0003-9321-0751","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB\u2013Technical University of Ostrava, Ostrava, Czech Republic","institution_ids":["https://openalex.org/I142208455"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056523918","display_name":"Petr Gajdo\u0161","orcid":"https://orcid.org/0000-0003-1831-3489","observed_orcids":["https://orcid.org/0000-0003-1831-3489"]},"institutions":[{"id":"https://openalex.org/I142208455","display_name":"VSB - Technical University of Ostrava","ror":"https://ror.org/05x8mcb75","country_code":"CZ","type":"education","lineage":["https://openalex.org/I142208455"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Petr Gajd\u0161","raw_affiliation_strings":["Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB\u2013Technical University of Ostrava, Ostrava, Czech Republic"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB\u2013Technical University of Ostrava, Ostrava, Czech Republic","institution_ids":["https://openalex.org/I142208455"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5053887935"],"corresponding_institution_ids":["https://openalex.org/I142208455"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.513700008392334,"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"}},"topics":[{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.513700008392334,"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.3978999853134155,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.06509999930858612,"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/blood-cell-classification","display_name":"blood cell classification","score":0.6039999723434448},{"id":"https://openalex.org/keywords/vision-transformer","display_name":"vision transformer","score":0.7139999866485596},{"id":"https://openalex.org/keywords/resnet-50","display_name":"ResNet50","score":0.984000027179718},{"id":"https://openalex.org/keywords/microscopy-images","display_name":"microscopic images","score":0.4169999957084656},{"id":"https://openalex.org/keywords/benchmark-reproducibility","display_name":"benchmark reproducibility","score":0.6129999756813049}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6244999766349792},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5878999829292297},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5515000224113464},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5325999855995178},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4399999976158142},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4327000081539154},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.43130001425743103},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4108000099658966},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3968999981880188},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38659998774528503},{"id":"https://openalex.org/C44648626","wikidata":"https://www.wikidata.org/wiki/Q1049848","display_name":"Percentage point","level":2,"score":0.38339999318122864},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.3799000084400177},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.36649999022483826},{"id":"https://openalex.org/C166955791","wikidata":"https://www.wikidata.org/wiki/Q629579","display_name":"Macro","level":2,"score":0.34950000047683716},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3384000062942505},{"id":"https://openalex.org/C137345334","wikidata":"https://www.wikidata.org/wiki/Q7303350","display_name":"Recursive partitioning","level":2,"score":0.3181000053882599},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.3138999938964844},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3098999857902527},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3028999865055084},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.2892000079154968},{"id":"https://openalex.org/C2781415353","wikidata":"https://www.wikidata.org/wiki/Q5196602","display_name":"Cut-point","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C2781311116","wikidata":"https://www.wikidata.org/wiki/Q83306","display_name":"Group (periodic table)","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2705000042915344},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.251800000667572},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.64898/2026.10.01.755883","doi":"https://doi.org/10.64898/2026.10.01.755883","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.10.01.755883","pdf_url":"https://www.biorxiv.org/content/biorxiv/early/2026/10/07/2026.10.01.755883.full.pdf","source":{"id":"https://openalex.org/S4306402567","display_name":"bioRxiv (Cold Spring Harbor Laboratory)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2750212522","host_organization_name":"Cold Spring Harbor Laboratory","host_organization_lineage":["https://openalex.org/I2750212522"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"bioRxiv","raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.64898/2026.10.01.755883","doi":"https://doi.org/10.64898/2026.10.01.755883","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.10.01.755883","pdf_url":"https://www.biorxiv.org/content/biorxiv/early/2026/10/07/2026.10.01.755883.full.pdf","source":{"id":"https://openalex.org/S4306402567","display_name":"bioRxiv (Cold Spring Harbor Laboratory)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2750212522","host_organization_name":"Cold Spring Harbor Laboratory","host_organization_lineage":["https://openalex.org/I2750212522"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"bioRxiv","raw_type":"posted-content"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7221032865.pdf","grobid_xml":"https://content.openalex.org/works/W7221032865.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W2986235590","https://openalex.org/W3015930014","https://openalex.org/W3194282450","https://openalex.org/W4207016125","https://openalex.org/W4221151810","https://openalex.org/W4317436377","https://openalex.org/W4385576721","https://openalex.org/W4403247620","https://openalex.org/W4403649789","https://openalex.org/W4416388581","https://openalex.org/W6940528644","https://openalex.org/W6958813636","https://openalex.org/W7118654018","https://openalex.org/W7161272692","https://openalex.org/W7213510949"],"related_works":["https://openalex.org/W7214892968","https://openalex.org/W2975035192","https://openalex.org/W7216409108","https://openalex.org/W7204939779","https://openalex.org/W4410295098","https://openalex.org/W7204437003","https://openalex.org/W7167588538","https://openalex.org/W7160306460","https://openalex.org/W7222922338","https://openalex.org/W7221050591"],"abstract_inverted_index":{"Abstract":[0],"Machine":[1],"learning":[2],"benchmarks":[3],"often":[4,47],"treat":[5],"image":[6],"files":[7,13,139,237],"as":[8,240],"independent":[9],"observations,":[10],"although":[11,218],"multiple":[12,86],"may":[14],"represent":[15],"repeated":[16],"views":[17],"or":[18],"other":[19],"related":[20],"samples.":[21],"Prior":[22],"work":[23],"has":[24],"shown":[25],"that":[26,206],"non-independent":[27],"partitioning":[28,93],"can":[29],"inflate":[30],"model":[31,115],"performance,":[32],"but":[33],"the":[34,109,141,158,167,180,195,211,215,219],"sensitivity":[35],"of":[36,104,123,137,210,246],"a":[37,81,131,176],"biomedical":[38],"benchmark":[39],"to":[40,64,101,117,148,154,157,242],"progressively":[41,62,165],"increasing":[42],"train\u2013test":[43],"relatedness":[44],"is":[45],"less":[46],"quantified.":[48],"Here,":[49],"we":[50,76],"quantified":[51],"how":[52],"apparent":[53,182],"classification":[54],"performance":[55,163],"changes":[56],"when":[57],"recoverable":[58],"sample":[59,248],"groups":[60,79],"are":[61,238],"allowed":[63],"cross":[65],"training,":[66],"validation,":[67],"and":[68,74,88,126,150,179,188,221,234],"test":[69,143,204],"partitions.":[70],"Using":[71],"source":[72],"structure":[73],"filenames,":[75],"reconstructed":[77],"analysis":[78,193],"in":[80,185,214],"microscopy":[82],"corpus":[83],"compiled":[84],"from":[85,96,146,152],"sources":[87],"generated":[89],"five":[90],"class":[91],"stratified":[92],"conditions,":[94],"ranging":[95],"an":[97],"intact":[98,159],"group":[99,160,213],"reference":[100],"random":[102,135,196],"assignment":[103,136,197],"individual":[105,138],"files.":[106],"We":[107],"applied":[108],"same":[110,212],"training":[111,216],"recipe":[112],"within":[113,194],"each":[114],"family":[116],"129,173":[118],"images":[119,205],"spanning":[120],"14":[121],"classes":[122],"blood":[124],"cells":[125],"hematopoietic":[127],"cell":[128],"states.":[129],"For":[130],"pretrained":[132],"Vision":[133],"Transformer,":[134],"increased":[140,164],"mean":[142],"macro":[144],"F1":[145],"0.853":[147],"0.977":[149],"accuracy":[151],"0.851":[153],"0.990":[155],"relative":[156],"reference.":[161],"Apparent":[162],"across":[166],"intermediate":[168],"conditions.":[169],"The":[170,227],"endpoint":[171],"pattern":[172],"also":[173],"appeared":[174],"with":[175],"ResNet-50":[177],"backbone,":[178],"largest":[181],"gains":[183],"occurred":[184],"immature":[186],"granulocytic":[187],"blast":[189],"classes.":[190],"A":[191],"descriptive":[192],"condition":[198],"further":[199],"showed":[200],"higher":[201],"scores":[202],"among":[203],"had":[207],"another":[208],"member":[209],"set,":[217],"exposed":[220],"unexposed":[222],"subsets":[223],"were":[224],"not":[225],"exchangeable.":[226],"grouping":[228],"workflow,":[229],"exact":[230],"split":[231],"definitions,":[232],"code,":[233],"compact":[235],"result":[236],"released":[239],"HSD-Bench":[241],"support":[243],"reproducible":[244],"evaluation":[245],"hidden":[247],"dependence.":[249]},"counts_by_year":[],"updated_date":"2026-11-09T13:22:03.289127","created_date":"2026-10-09T00:00:00"}
