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Deutges, M. ; Sadafi, A. ; Navab, N.* ; Marr, C.

Neural cellular automata for lightweight, robust and explainable classification of white blood cell images.

In: (Medical Image Computing and Computer Assisted Intervention – MICCAI 2024). Berlin [u.a.]: Springer, 2024. 693-702 (Lect. Notes Comput. Sc. ; 15003)
DOI
Diagnosis of hematological malignancies depends on accurate identification of white blood cells in peripheral blood smears. Deep learning techniques are emerging as a viable solution to scale and optimize this process by automatic cell classification. However, these techniques face several challenges such as limited generalizability, sensitivity to domain shifts, and lack of explainability. Here, we introduce a novel approach for white blood cell classification based on neural cellular automata (NCA). We test our approach on three datasets of white blood cell images and show that we achieve competitive performance compared to conventional methods. Our NCA-based method is significantly smaller in terms of parameters and exhibits robustness to domain shifts. Furthermore, the architecture is inherently explainable, providing insights into the decision process for each classification, which helps to understand and validate model predictions. Our results demonstrate that NCA can be used for image classification, and that they address key challenges of conventional methods, indicating a high potential for applicability in clinical practice.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Neural Cellular Automata; Explainability; Single-Cell Classification; Domain Generalization
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Medical Image Computing and Computer Assisted Intervention – MICCAI 2024
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 15003, Heft: , Seiten: 693-702 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute of AI for Health (AIH)
Förderungen Hightech Agenda Bayern