{"id":"https://openalex.org/W7118778273","doi":"https://doi.org/10.48550/arxiv.2601.02198","title":"Mind the Gap: Continuous Magnification Sampling for Pathology Foundation Models","display_name":"Mind the Gap: Continuous Magnification Sampling for Pathology Foundation Models","publication_year":2026,"publication_date":"2026-01-05","ids":{"openalex":"https://openalex.org/W7118778273","doi":"https://doi.org/10.48550/arxiv.2601.02198"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.02198","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.02198","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2601.02198","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122072244","display_name":"Alexander M\u00f6llers","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"M\u00f6llers, Alexander","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084914129","display_name":"Julius Hense","orcid":"https://orcid.org/0009-0007-1160-1636"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hense, Julius","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054911686","display_name":"F. Schulz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schulz, Florian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5094218811","display_name":"Timo Milbich","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Milbich, Timo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122145525","display_name":"Maximilian Alber","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alber, Maximilian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5016979661","display_name":"Lukas Ruff","orcid":"https://orcid.org/0000-0002-9707-297X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruff, Lukas","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":0.9398000240325928,"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":0.9398000240325928,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.00430000014603138,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.003700000001117587,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/magnification","display_name":"Magnification","score":0.927299976348877},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.7265999913215637},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.532800018787384},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.39070001244544983},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.3709000051021576},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.349700003862381}],"concepts":[{"id":"https://openalex.org/C4144372","wikidata":"https://www.wikidata.org/wiki/Q675287","display_name":"Magnification","level":2,"score":0.927299976348877},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.7265999913215637},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6162999868392944},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.532800018787384},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.39070001244544983},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3709000051021576},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36820000410079956},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.349700003862381},{"id":"https://openalex.org/C2777522853","wikidata":"https://www.wikidata.org/wiki/Q5276128","display_name":"Digital pathology","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30790001153945923},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2957000136375427},{"id":"https://openalex.org/C167723999","wikidata":"https://www.wikidata.org/wiki/Q3773214","display_name":"Sampling distribution","level":2,"score":0.2955999970436096},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.27570000290870667},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.25769999623298645}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.02198","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.02198","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2601.02198","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.02198","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":"Preprint"},"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":{"In":[0],"histopathology,":[1],"pathologists":[2],"examine":[3],"both":[4],"tissue":[5],"architecture":[6],"at":[7,13,78,95,135],"low":[8],"magnification":[9,29,38,84,90,108,167],"and":[10,25,46,150],"fine-grained":[11],"morphology":[12],"high":[14],"magnification.":[15],"Yet,":[16],"the":[17,26,62,180],"performance":[18,94,173],"of":[19,28,68,140,172],"pathology":[20,184],"foundation":[21,163,185],"models":[22,186],"across":[23,107,175,190],"magnifications":[24,69],"effect":[27],"sampling":[30,39,57,67,101,129,134],"during":[31],"training":[32],"remain":[33],"poorly":[34],"understood.":[35],"We":[36,59,81],"model":[37],"as":[40],"a":[41,48,169],"multi-source":[42],"domain":[43],"adaptation":[44],"problem":[45],"develop":[47],"simple":[49],"theoretical":[50],"framework":[51],"that":[52,61,103,127,151,166,187],"reveals":[53],"systematic":[54],"trade-offs":[55],"between":[56],"strategies.":[58],"show":[60,126],"widely":[63],"used":[64],"discrete":[65,133],"uniform":[66],"(0.25,":[70],"0.5,":[71],"1.0,":[72],"2.0":[73],"mpp)":[74],"leads":[75],"to":[76,142],"degradation":[77],"intermediate":[79,136],"magnifications.":[80,191],"introduce":[82,115],"continuous":[83,128],"sampling,":[85],"which":[86],"removes":[87],"gaps":[88],"in":[89,146],"coverage":[91],"while":[92],"preserving":[93],"standard":[96],"scales.":[97,109],"Further,":[98],"we":[99,114,159],"derive":[100],"distributions":[102,153],"optimize":[104],"representation":[105],"quality":[106],"To":[110],"evaluate":[111,160],"these":[112],"strategies,":[113],"two":[116],"new":[117],"benchmarks":[118],"(TCGA-MS,":[119],"BRACS-MS)":[120],"with":[121,138],"appropriate":[122],"metrics.":[123],"Our":[124,177],"experiments":[125],"substantially":[130],"improves":[131],"over":[132],"magnifications,":[137],"gains":[139],"up":[141],"4":[143],"percentage":[144],"points":[145],"balanced":[147],"classification":[148],"accuracy,":[149],"optimized":[152],"can":[154],"further":[155],"improve":[156],"performance.":[157],"Finally,":[158],"current":[161],"histopathology":[162],"models,":[164],"finding":[165],"is":[168],"primary":[170],"driver":[171],"variation":[174],"models.":[176],"work":[178],"paves":[179],"way":[181],"towards":[182],"future":[183],"perform":[188],"reliably":[189]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-01-08T00:00:00"}
