{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T09:50:06Z","timestamp":1789206606232,"version":"build-2803163510"},"reference-count":27,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cytometry Part B Clinical"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Flow cytometry is an essential component of routine hematological lab testing. Many computational methods have been proposed for the analysis of flow cytometry data, but most have focused on supervised learning for just one or a few specific disorders. To maximize clinical utility, we develop a method that enables identification of multiple common disorders and quality indicators. Our method includes a self\u2010supervised pretraining component as well as a new, transformer\u2010based model architecture. The self\u2010supervised training algorithm is based on the DINO method while the model architecture is a relatively simple transformer encoder stack that includes a class (CLS) token, similar to BERT or vision\u2010transformer models. Using a dataset of 52,625 samples obtained during routine clinical testing at our laboratory, we show that our pretraining method develops informative tube\u2010level representations that clearly separate important diagnostic classes. We then evaluate performance on multiple downstream tasks, including sample viability estimation and five common hematological disorders. We compare our method to self\u2010organizing maps, convolutional neural networks, attention\u2010based multiple\u2010instance learning models, and two varieties of set\u2010transformer\u2010based models, and demonstrate that our method delivers higher classification performance than other approaches.<\/jats:p>","DOI":"10.1002\/cyto.b.70043","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T11:42:22Z","timestamp":1781523742000},"page":"347-358","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["<scp>DinoFlow<\/scp>\n                    : Self\u2010supervised pretraining in flow cytometry enables accurate detection of common hematopathological disorders"],"prefix":"10.1002","volume":"110","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7185-7894","authenticated-orcid":false,"given":"Brendan","family":"O'Fallon","sequence":"first","affiliation":[{"name":"ARUP Institute for Research and Innovation ARUP Laboratories  Salt Lake City Utah USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muir","family":"Morrison","sequence":"additional","affiliation":[{"name":"ARUP Institute for Research and Innovation ARUP Laboratories  Salt Lake City Utah USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mattia Medina","family":"Grespan","sequence":"additional","affiliation":[{"name":"ARUP Institute for Research and Innovation ARUP Laboratories  Salt Lake City Utah USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas C.","family":"Spies","sequence":"additional","affiliation":[{"name":"ARUP Institute for Research and Innovation ARUP Laboratories  Salt Lake City Utah USA"},{"name":"Department of Pathology University of Utah  Salt Lake City Utah USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8604-2073","authenticated-orcid":false,"given":"David P.","family":"Ng","sequence":"additional","affiliation":[{"name":"ARUP Institute for Research and Innovation ARUP Laboratories  Salt Lake City Utah USA"},{"name":"Department of Pathology University of Utah  Salt Lake City Utah USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,15]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007910920355"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1408792111"},{"key":"e_1_2_7_4_1","first-page":"9650","volume-title":"Proceedings of the IEEE\/CVF international conference on computer vision","author":"Caron M.","year":"2021"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.59"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1423"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2003026117"},{"key":"e_1_2_7_8_1","first-page":"2127","volume-title":"International conference on machine learning","author":"Ilse M.","year":"2018"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.xcrm.2024.101808"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.12688\/f1000research.21642.2"},{"key":"e_1_2_7_11_1","first-page":"3744","volume-title":"International conference on machine learning","author":"Lee J.","year":"2019"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2015.05.047"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.modpat.2023.100373"},{"key":"e_1_2_7_14_1","unstructured":"McInnes L. Healy J. &Melville J.(2020).Umap: Uniform manifold approximation and projection for dimension reduction.https:\/\/arxiv.org\/abs\/1802.03426"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.a.24566"},{"key":"e_1_2_7_16_1","article-title":"Dinov2: Learning robust visual features without supervision","author":"Oquab M.","year":"2023","journal-title":"arXiv Preprint"},{"key":"e_1_2_7_17_1","first-page":"8024","volume-title":"Advances in neural information processing systems","author":"Paszke A.","year":"2019"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41596-021-00550-0"},{"key":"e_1_2_7_19_1","first-page":"8748","volume-title":"International conference on machine learning","author":"Radford A.","year":"2021"},{"key":"e_1_2_7_20_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btad585"},{"key":"e_1_2_7_21_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14102537"},{"key":"e_1_2_7_22_1","article-title":"Glu variants improve transformer","author":"Shazeer N.","year":"2020","journal-title":"arXiv Preprint"},{"key":"e_1_2_7_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.a.22625"},{"key":"e_1_2_7_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00777"},{"key":"e_1_2_7_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-78198-8_21"},{"key":"e_1_2_7_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105314"},{"key":"e_1_2_7_27_1","article-title":"Virchow2: Scaling self\u2010supervised mixed magnification models in pathology","author":"Zimmermann E.","year":"2024","journal-title":"arXiv Preprint"},{"key":"e_1_2_7_28_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.b.22229"}],"container-title":["Cytometry Part B: Clinical Cytometry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cyto.b.70043","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/cyto.b.70043","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cyto.b.70043","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T21:51:43Z","timestamp":1789163503000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/cyto.b.70043"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,15]]},"references-count":27,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["10.1002\/cyto.b.70043"],"URL":"https:\/\/doi.org\/10.1002\/cyto.b.70043","archive":["Portico"],"relation":{},"ISSN":["1552-4949","1552-4957"],"issn-type":[{"value":"1552-4949","type":"print"},{"value":"1552-4957","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,15]]},"assertion":[{"value":"2025-11-13","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-05-21","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-06-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}