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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 as soon as Postprint is submitted to ZB.
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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Publication type Article: Conference contribution
Corresponding Author
Keywords Attention ; Multiple Instance Learning ; Red Blood Cells
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 12265 LNCS, Issue: , Pages: 246-256 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Non-patent literature Publications
Institute(s) Institute of Computational Biology (ICB)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Helmholtz AI - HMGU (HAI - HMGU)