{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T23:21:12Z","timestamp":1773876072575,"version":"3.50.1"},"reference-count":37,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"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 Pt A"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>We present\u00a0a new end\u2010to\u2010end neural network approach for real\u2010time biological cell detection and classification via label\u2010free quantitative imaging flow cytometry based on digital holography, offering a comprehensive representation of cellular structures without the need for chemical cell staining. In contrast to previous studies, our method is the first to obtain classification and detection of cells, imaged during flow using large\u2010magnification microscopy, in 0.44\u2009msec, allowing real\u2010time label\u2010free imaging flow cytometry, with more than 10\u00d7 speedup compared to YOLOv8n. The custom\u2010made two\u2010stage neural network consists of fixed convolution layers using image processing filters to detect a single location per object, followed by two convolutional layers that classify each detected cell. This approach enables reducing computational complexity and offers high\u2010throughput, label\u2010free imaging\u2010based analysis suitable for real\u2010time imaging flow cytometry. We validate the method on two cell datasets, T\u2010cells at different activation stages and cancer cells of different metastatic potentials, demonstrating the method's adaptability. Our results show the ability to image, detect, and classify thousands of cells per second during flow, highlighting the potential of label\u2010free imaging flow cytometry for real\u2010time cell monitoring, early disease detection, and high\u2010speed diagnostics.<\/jats:p>","DOI":"10.1002\/cytoa.70008","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T09:33:06Z","timestamp":1769506386000},"page":"89-97","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Label\u2010Free Holographic Imaging Flow Cytometry With Deep\u2010Learning\u2010Based Detection and Classification of Thousands of Cells Per Second"],"prefix":"10.1002","volume":"109","author":[{"given":"Dana","family":"Yagoda\u2010Aharoni","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering Faculty of Engineering, Tel Aviv University  Tel Aviv Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eden","family":"Dotan","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering Faculty of Engineering, Tel Aviv University  Tel Aviv Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matan","family":"Dudaie","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering Faculty of Engineering, Tel Aviv University  Tel Aviv Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Natan T.","family":"Shaked","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering Faculty of Engineering, Tel Aviv University  Tel Aviv Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,1,27]]},"reference":[{"key":"e_1_2_8_2_1","first-page":"19","volume-title":"Methods in Cell Biology","author":"Givan A. 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