Comprehensive bone marrow analysis integrating deep learning-based pattern discovery (BMDeep)

Pontones M, Hoefener H, Kock F, Schwen L, Westphal M, Dickel N, Kunz M, Metzler M (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

Klinische Pädiatrie Georg Thieme Verlag

Publisher: GEORG THIEME VERLAG KG

City/Town: STUTTGART

Pages Range: 190-190

Conference Proceedings Title: KLINISCHE PADIATRIE

DOI: 10.1055/s-0042-1748749

Abstract

Bone marrow morphology forms the basis for the assessment of hematopoiesis. The currently established approach is dependent on manual microscopic counting of a limited number of cells by specially trained personnel, which is inherently associated with substantial intra- and inter-individual variability. The aim of our project is to automatize and improve the evaluation of bone marrow smears and to identify pathological patterns in pediatric leukemia.

Authors with CRIS profile

Martina Pontones Department of Paediatrics and Adolescent Medicine Nicholas Dickel Lehrstuhl für Medizinische Informatik Meik Kunz Lehrstuhl für Medizinische Informatik Markus Metzler Professur für Kinder- und Jugendmedizin mit dem Schwerpunkt Pädiatrische Onkologie und Hämatologie

Involved external institutions

Fraunhofer-Institut für Bildgestützte Medizin (MEVIS) DE Germany (DE)

How to cite

APA:

Pontones, M., Hoefener, H., Kock, F., Schwen, L., Westphal, M., Dickel, N.,... Metzler, M. (2022). Comprehensive bone marrow analysis integrating deep learning-based pattern discovery (BMDeep). In KLINISCHE PADIATRIE (pp. 190-190). STUTTGART: GEORG THIEME VERLAG KG.

MLA:

Pontones, Martina, et al. "Comprehensive bone marrow analysis integrating deep learning-based pattern discovery (BMDeep)." Proceedings of the KLINISCHE PADIATRIE STUTTGART: GEORG THIEME VERLAG KG, 2022. 190-190.

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