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A deep-learning algorithm (AIFORIA) for classification of hematopoietic cells in bone marrow aspirate smears based on nine cell classes-a feasible approach for routine screening?

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Title: A deep-learning algorithm (AIFORIA) for classification of hematopoietic cells in bone marrow aspirate smears based on nine cell classes-a feasible approach for routine screening?
Authors: Saft L; Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden. leonie.saft@regionstockholm.se.; Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden. leonie.saft@regionstockholm.se., Vaara E; Aiforia Technologies Plc, Helsinki, Finland., Ljung E; Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden.; Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden., Kwiecinska A; Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden.; Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden., Kumar D; Aiforia Technologies Plc, Helsinki, Finland., Timar B; 1st Department of Pathology and Experimental Cancer Research, Semmelweis University, Budapest, Hungary.
Source: Journal of hematopathology [J Hematop] 2025 Mar 29; Vol. 18 (1), pp. 12. Date of Electronic Publication: 2025 Mar 29.
Publication Type: Journal Article
Journal Info: Publisher: Springer Country of Publication: Germany NLM ID: 101491976 Publication Model: Electronic Cited Medium: Internet ISSN: 1865-5785 (Electronic) Linking ISSN: 18655785 NLM ISO Abbreviation: J Hematop Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: A deep-learning algorithm (AIFORIA) for classification of hematopoietic cells in bone marrow aspirate smears based on nine cell classes-a feasible approach for routine screening?
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  Data: <searchLink fieldCode="AU" term="%22Saft+L%22">Saft L</searchLink>; Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden. leonie.saft@regionstockholm.se.; Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden. leonie.saft@regionstockholm.se.<br /><searchLink fieldCode="AU" term="%22Vaara+E%22">Vaara E</searchLink>; Aiforia Technologies Plc, Helsinki, Finland.<br /><searchLink fieldCode="AU" term="%22Ljung+E%22">Ljung E</searchLink>; Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden.; Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.<br /><searchLink fieldCode="AU" term="%22Kwiecinska+A%22">Kwiecinska A</searchLink>; Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden.; Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.<br /><searchLink fieldCode="AU" term="%22Kumar+D%22">Kumar D</searchLink>; Aiforia Technologies Plc, Helsinki, Finland.<br /><searchLink fieldCode="AU" term="%22Timar+B%22">Timar B</searchLink>; 1st Department of Pathology and Experimental Cancer Research, Semmelweis University, Budapest, Hungary.
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  Data: <searchLink fieldCode="JN" term="%22101491976%22">Journal of hematopathology</searchLink> [J Hematop] 2025 Mar 29; Vol. 18 (1), pp. 12. <i>Date of Electronic Publication: </i>2025 Mar 29.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Springer%22">Springer </searchLink><i>Country of Publication: </i>Germany <i>NLM ID: </i>101491976 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1865-5785 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2218655785%22">18655785 </searchLink><i>NLM ISO Abbreviation: </i>J Hematop <i>Subsets: </i>MEDLINE
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