{"id":"https://openalex.org/W4221161782","doi":"https://doi.org/10.48550/arxiv.2203.00585","title":"Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology","display_name":"Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology","publication_year":2022,"publication_date":"2022-03-01","ids":{"openalex":"https://openalex.org/W4221161782","doi":"https://doi.org/10.48550/arxiv.2203.00585"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2203.00585","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.00585","pdf_url":"https://arxiv.org/pdf/2203.00585","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2203.00585","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054665212","display_name":"Richard J. Chen","orcid":"https://orcid.org/0000-0003-0389-1331"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Richard J.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5073514348","display_name":"Rahul G. Krishnan","orcid":"https://orcid.org/0000-0002-7955-3956"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Krishnan, Rahul G.","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":43,"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":0.9991999864578247,"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":0.9991999864578247,"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/T11316","display_name":"Mycobacterium research and diagnosis","score":0.9829000234603882,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.9818000197410583,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.762677013874054},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.699124813079834},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5431926846504211},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4525541365146637},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.442238450050354},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.43876686692237854},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4320198893547058},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.4189218580722809},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1153729259967804}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.762677013874054},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.699124813079834},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5431926846504211},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4525541365146637},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.442238450050354},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.43876686692237854},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4320198893547058},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4189218580722809},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1153729259967804}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2203.00585","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.00585","pdf_url":"https://arxiv.org/pdf/2203.00585","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2203.00585","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2203.00585","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":"pmh:oai:arXiv.org:2203.00585","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.00585","pdf_url":"https://arxiv.org/pdf/2203.00585","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.5600000023841858}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2032233321","https://openalex.org/W3121970507","https://openalex.org/W2110028391","https://openalex.org/W54497855","https://openalex.org/W217960748","https://openalex.org/W3125814499","https://openalex.org/W4285328440","https://openalex.org/W4390062853","https://openalex.org/W4389256085","https://openalex.org/W4313644201"],"abstract_inverted_index":{"Tissue":[0],"phenotyping":[1],"is":[2,24,122],"a":[3,25,95,104,112],"fundamental":[4],"task":[5],"in":[6,17,29,42,52,57,78,100,123,140],"learning":[7,73],"objective":[8],"characterizations":[9],"of":[10,48,106,114],"histopathologic":[11],"biomarkers":[12],"within":[13],"the":[14,144],"tumor-immune":[15],"microenvironment":[16],"cancer":[18],"pathology.":[19],"However,":[20],"whole-slide":[21],"imaging":[22],"(WSI)":[23],"complex":[26],"computer":[27],"vision":[28],"which:":[30],"1)":[31],"WSIs":[32],"have":[33,66,85],"enormous":[34],"image":[35,70],"resolutions":[36],"with":[37,109],"precludes":[38],"large-scale":[39],"pixel-level":[40],"efforts":[41,65],"data":[43],"curation,":[44],"and":[45,54,116,137,156],"2)":[46],"diversity":[47],"morphological":[49,80,150],"phenotypes":[50],"results":[51],"inter-":[53],"intra-observer":[55],"variability":[56],"tissue":[58],"labeling.":[59],"To":[60],"address":[61],"these":[62],"limitations,":[63],"current":[64],"proposed":[67],"using":[68,128],"pretrained":[69,157],"encoders":[71],"(transfer":[72],"from":[74,82],"ImageNet,":[75],"self-supervised":[76,107],"pretraining)":[77],"extracting":[79],"features":[81,139],"pathology,":[83],"but":[84],"not":[86],"been":[87],"extensively":[88],"validated.":[89],"In":[90],"this":[91],"work,":[92],"we":[93],"conduct":[94],"search":[96],"for":[97],"good":[98],"representations":[99],"pathology":[101],"by":[102],"training":[103],"variety":[105,113],"models":[108],"validation":[110],"on":[111],"weakly-supervised":[115],"patch-level":[117],"tasks.":[118],"Our":[119],"key":[120],"finding":[121],"discovering":[124],"that":[125],"Vision":[126],"Transformers":[127],"DINO-based":[129],"knowledge":[130],"distillation":[131],"are":[132],"able":[133],"to":[134],"learn":[135,148],"data-efficient":[136],"interpretable":[138],"histology":[141],"images":[142],"wherein":[143],"different":[145],"attention":[146],"heads":[147],"distinct":[149],"phenotypes.":[151],"We":[152],"make":[153],"evaluation":[154],"code":[155],"weights":[158],"publicly-available":[159],"at:":[160],"https://github.com/Richarizardd/Self-Supervised-ViT-Path.":[161]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":4}],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2022-04-03T00:00:00"}
