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Kazeminia, S. ; Sadafi, A. ; Makhro, A.* ; Bogdanova, A.* ; Albarqouni, S. ; Marr, C.

Anomaly-aware multiple instance learning for rare anemia disorder classification.

Lect. Notes Comput. Sc. 13438 LNCS, 341-350 (2022)
Postprint DOI
Open Access Green
Deep learning-based classification of rare anemia disorders is challenged by the lack of training data and instance-level annotations. Multiple Instance Learning (MIL) has shown to be an effective solution, yet it suffers from low accuracy and limited explainability. Although the inclusion of attention mechanisms has addressed these issues, their effectiveness highly depends on the amount and diversity of cells in the training samples. Consequently, the poor machine learning performance on rare anemia disorder classification from blood samples remains unresolved. In this paper, we propose an interpretable pooling method for MIL to address these limitations. By benefiting from instance-level information of negative bags (i.e., homogeneous benign cells from healthy individuals), our approach increases the contribution of anomalous instances. We show that our strategy outperforms standard MIL classification algorithms and provides a meaningful explanation behind its decisions. Moreover, it can denote anomalous instances of rare blood diseases that are not seen during the training phase.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Anomaly Pooling ; Multiple Instance Learning ; Rare Anemia Disorder ; Red Blood Cells
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Conference Title Medical Image Computing and Computer Assisted Intervention – MICCAI 2022
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 13438 LNCS, Issue: , Pages: 341-350 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Non-patent literature Publications
Institute(s) Institute of AI for Health (AIH)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)