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Sadafi, A. ; Makhro, A.* ; Bogdanova, A.* ; Navab, N.* ; Peng, T. ; Albarqouni, S. ; Marr, C.

Attention based multiple instance learning for classification of blood cell disorders.

In:. Berlin [u.a.]: Springer, 2020. 246-256 (Lect. Notes Comput. Sc. ; 12265 LNCS)
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Red blood cells are highly deformable and present in various shapes. In blood cell disorders, only a subset of all cells is morphologically altered and relevant for the diagnosis. However, manually labeling of all cells is laborious, complicated and introduces inter-expert variability. We propose an attention based multiple instance learning method to classify blood samples of patients suffering from blood cell disorders. Cells are detected using an R-CNN architecture. With the features extracted for each cell, a multiple instance learning method classifies patient samples into one out of four blood cell disorders. The attention mechanism provides a measure of the contribution of each cell to the overall classification and significantly improves the networks classification accuracy as well as its interpretability for the medical expert.
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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Attention ; Multiple Instance Learning ; Red Blood Cells
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Quellenangaben Band: 12265 LNCS, Heft: , Seiten: 246-256 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]