{"id":"https://openalex.org/W4396650787","doi":"https://doi.org/10.48550/arxiv.2405.00908","title":"Transformer-Based Self-Supervised Learning for Histopathological Classification of Ischemic Stroke Clot Origin","display_name":"Transformer-Based Self-Supervised Learning for Histopathological Classification of Ischemic Stroke Clot Origin","publication_year":2024,"publication_date":"2024-05-01","ids":{"openalex":"https://openalex.org/W4396650787","doi":"https://doi.org/10.48550/arxiv.2405.00908"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2405.00908","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.00908","pdf_url":"https://arxiv.org/pdf/2405.00908","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":"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/2405.00908","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032141751","display_name":"Kuo\u2010Wei Yeh","orcid":"https://orcid.org/0000-0002-2499-8050"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yeh, K.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022563719","display_name":"Mohamed Sobhi Jabal","orcid":"https://orcid.org/0000-0003-2891-8633"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jabal, M. S.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079422276","display_name":"Vikas Gupta","orcid":"https://orcid.org/0000-0001-6462-0397"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gupta, V.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090296288","display_name":"David F. Kallmes","orcid":"https://orcid.org/0000-0002-8495-0040"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kallmes, D. F.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051049858","display_name":"Waleed Brinjikji","orcid":"https://orcid.org/0000-0001-5271-5524"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Brinjikji, W.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5081218376","display_name":"Barbaros S. Erdal","orcid":"https://orcid.org/0000-0003-3637-0102"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Erdal, B. S.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5032141751"],"corresponding_institution_ids":[],"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":0.988099992275238,"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":0.988099992275238,"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/T10862","display_name":"AI in cancer detection","score":0.9864000082015991,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9843000173568726,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/ischemic-stroke","display_name":"Ischemic stroke","score":0.5949438810348511},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.45780205726623535},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.45705389976501465},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43135517835617065},{"id":"https://openalex.org/keywords/stroke","display_name":"Stroke (engine)","score":0.4254275858402252},{"id":"https://openalex.org/keywords/cardiology","display_name":"Cardiology","score":0.40926021337509155},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32811450958251953},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.326526403427124},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.32202669978141785},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2262761890888214},{"id":"https://openalex.org/keywords/ischemia","display_name":"Ischemia","score":0.19618895649909973},{"id":"https://openalex.org/keywords/mechanical-engineering","display_name":"Mechanical engineering","score":0.07949188351631165},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.07517063617706299}],"concepts":[{"id":"https://openalex.org/C3020199598","wikidata":"https://www.wikidata.org/wiki/Q12202","display_name":"Ischemic stroke","level":3,"score":0.5949438810348511},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.45780205726623535},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.45705389976501465},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43135517835617065},{"id":"https://openalex.org/C2780645631","wikidata":"https://www.wikidata.org/wiki/Q671554","display_name":"Stroke (engine)","level":2,"score":0.4254275858402252},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.40926021337509155},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32811450958251953},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.326526403427124},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.32202669978141785},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2262761890888214},{"id":"https://openalex.org/C541997718","wikidata":"https://www.wikidata.org/wiki/Q188151","display_name":"Ischemia","level":2,"score":0.19618895649909973},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.07949188351631165},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.07517063617706299},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2405.00908","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.00908","pdf_url":"https://arxiv.org/pdf/2405.00908","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2405.00908","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2405.00908","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":"pmh:oai:arXiv.org:2405.00908","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.00908","pdf_url":"https://arxiv.org/pdf/2405.00908","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4396650787.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4387497383","https://openalex.org/W3183948672","https://openalex.org/W3173606202","https://openalex.org/W3110381201","https://openalex.org/W2948807893","https://openalex.org/W2935909890","https://openalex.org/W2778153218","https://openalex.org/W2758277628","https://openalex.org/W1531601525","https://openalex.org/W2460041365"],"abstract_inverted_index":{"Background":[0],"and":[1,14,77,92,100,102,123,152,176,212],"Purpose:":[2],"Identifying":[3],"the":[4,52,126,135,158,178],"thromboembolism":[5],"source":[6],"in":[7,29,121,167],"ischemic":[8,36,62,169],"stroke":[9,37,63,170],"is":[10,18,191],"crucial":[11],"for":[12,34,80,142,180,217],"treatment":[13],"secondary":[15],"prevention":[16],"yet":[17],"often":[19],"undetermined.":[20],"This":[21],"study":[22,156],"describes":[23],"a":[24,116],"self-supervised":[25,78],"deep":[26,69,164],"learning":[27,70,76,165],"approach":[28],"digital":[30],"pathology":[31],"of":[32,58,119,160,162],"emboli":[33],"classifying":[35,81],"clot":[38,143,171],"origin":[39,144],"from":[40,51,61,173],"histopathological":[41,174],"images.":[42],"Methods:":[43],"The":[44,113,155],"dataset":[45],"included":[46,84],"whole":[47],"slide":[48],"images":[49,175],"(WSI)":[50],"STRIP":[53],"AI":[54],"Kaggle":[55],"challenge,":[56],"consisting":[57],"retrieved":[59],"clots":[60],"patients":[64],"following":[65],"mechanical":[66],"thrombectomy.":[67],"Transformer-based":[68],"models":[71,166],"were":[72,98,105,132,146],"developed":[73],"using":[74,108],"transfer":[75],"pretraining":[79],"WSI.":[82,188],"Customizations":[83],"an":[85],"attention":[86],"pooling":[87],"layer,":[88],"weighted":[89,109],"loss":[90],"function,":[91],"threshold":[93],"optimization.":[94],"Various":[95],"model":[96,103,114,130,195],"architectures":[97],"tested":[99],"compared,":[101,133],"performances":[104],"primarily":[106],"evaluated":[107],"logarithmic":[110],"loss.":[111],"Results:":[112],"achieved":[115],"logloss":[117],"score":[118],"0.662":[120],"cross-validation":[122],"0.659":[124],"on":[125],"test":[127],"set.":[128],"Different":[129],"backbones":[131],"with":[134],"swin_large_patch4_window12_384":[136],"showed":[137],"higher":[138],"performance.":[139],"Thresholding":[140],"techniques":[141,183],"classification":[145],"employed":[147],"to":[148,186,193],"balance":[149],"false":[150],"positives":[151],"negatives.":[153],"Conclusion:":[154],"demonstrates":[157],"extent":[159],"efficacy":[161],"transformer-based":[163],"identifying":[168],"origins":[172],"emphasizes":[177],"need":[179],"refined":[181],"modeling":[182],"specifically":[184],"adapted":[185],"thrombi":[187],"Further":[189],"research":[190],"needed":[192],"improve":[194],"performance,":[196],"interpretability,":[197],"validate":[198],"its":[199],"effectiveness.":[200],"Future":[201],"enhancement":[202],"could":[203],"include":[204],"integrating":[205],"larger":[206],"patient":[207],"cohorts,":[208],"advanced":[209],"preprocessing":[210],"strategies,":[211],"exploring":[213],"ensemble":[214],"multimodal":[215],"methods":[216],"enhanced":[218],"diagnostic":[219],"accuracy.":[220]},"counts_by_year":[],"updated_date":"2026-03-11T14:59:36.786465","created_date":"2025-10-10T00:00:00"}