{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T11:13:31Z","timestamp":1792494811709,"version":"4.5.0"},"reference-count":23,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T00:00:00Z","timestamp":1591660800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"MD Stipend from Berlin Institute of Health"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cytometry Pt A"],"published-print":{"date-parts":[[2020,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    The wealth of information captured by multiparameter flow cytometry (MFC) can be analyzed by recent methods of computer vision when represented as a single image file. We therefore transformed MFC raw data into a multicolor 2D image by a self\u2010organizing map and classified this representation using a convolutional neural network. By this means, we built an artificial intelligence that is not only able to distinguish diseased from healthy samples, but it can also differentiate seven subtypes of mature B\u2010cell neoplasm. We trained our model with 18,274 cases including chronic lymphocytic leukemia and its precursor monoclonal B\u2010cell lymphocytosis, marginal zone lymphoma, mantle cell lymphoma, prolymphocytic leukemia, follicular lymphoma, hairy cell leukemia, lymphoplasmacytic lymphoma and achieved a weighted F1 score of 0.94 on a separate test set of 2,348 cases. Furthermore, we estimated the trustworthiness of a classification and could classify 70% of all cases with a confidence of 0.95 and higher. Our performance analyses indicate that particularly for rare subtypes further improvement can be expected when even more samples are available for training. \u00a9 2020 The Authors.\n                    <jats:italic>Cytometry Part A<\/jats:italic>\n                    published by Wiley Periodicals LLC. on behalf of International Society for Advancement of Cytometry.\n                  <\/jats:p>","DOI":"10.1002\/cyto.a.24159","type":"journal-article","created":{"date-parts":[[2020,6,10]],"date-time":"2020-06-10T02:39:00Z","timestamp":1591756740000},"page":"1073-1080","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["Hematologist\u2010Level Classification of Mature B\u2010Cell Neoplasm Using Deep Learning on Multiparameter Flow Cytometry Data"],"prefix":"10.1002","volume":"97","author":[{"given":"Max","family":"Zhao","sequence":"first","affiliation":[{"name":"Institute for Genomic Statistics and Bioinformatics University Bonn  Bonn Germany"},{"name":"Institute of Human Genetics and Medical Genetics Charit\u00e9 University Hospital  Berlin Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nanditha","family":"Mallesh","sequence":"additional","affiliation":[{"name":"Institute for Genomic Statistics and Bioinformatics University Bonn  Bonn Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"H\u00f6llein","sequence":"additional","affiliation":[{"name":"MLL Munich Leukemia Laboratory  Munich Germany"},{"name":"Red Cross Hospital Munich  Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Schabath","sequence":"additional","affiliation":[{"name":"MLL Munich Leukemia Laboratory  Munich Germany"},{"name":"Onkologische Praxis Berlin Mitte  Berlin Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claudia","family":"Haferlach","sequence":"additional","affiliation":[{"name":"MLL Munich Leukemia Laboratory  Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Torsten","family":"Haferlach","sequence":"additional","affiliation":[{"name":"MLL Munich Leukemia Laboratory  Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Franz","family":"Elsner","sequence":"additional","affiliation":[{"name":"res mechanica GmbH  Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hannes","family":"L\u00fcling","sequence":"additional","affiliation":[{"name":"res mechanica GmbH  Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3194-8625","authenticated-orcid":false,"given":"Peter","family":"Krawitz","sequence":"additional","affiliation":[{"name":"Institute for Genomic Statistics and Bioinformatics University Bonn  Bonn Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wolfgang","family":"Kern","sequence":"additional","affiliation":[{"name":"MLL Munich Leukemia Laboratory  Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,6,9]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.a.23030"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.2365"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2553401"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-bioeng-071516-044442"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.a.22625"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.58325"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0279-0"},{"key":"e_1_2_9_9_1","volume-title":"Deep Learning","author":"Goodfellow I","year":"2016"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco_a_00990"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.990110303"},{"key":"e_1_2_9_12_1","unstructured":"GormanC.A Multi\u2010Gpu Implementation of the Self\u2010Organizing Map in TensorFlow: Cgorman\/Tensorflow\u2010Som.2019. 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