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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)
Preprint DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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
Korrespondenzautor
Schlagwörter Attention ; Multiple Instance Learning ; Red Blood Cells
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 12265 LNCS, Heft: , Seiten: 246-256 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute of Computational Biology (ICB)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Helmholtz AI - HMGU (HAI - HMGU)