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

Hierarchical Neural Cellular Automata for Lightweight Microscopy Image Classification.

In: (29th International Conference on Information Processing in Medical Imaging, IPMI 2025, 25-30 May 2025, Kos). Berlin [u.a.]: Springer, 2026. 19-32 (Lect. Notes Comput. Sc. ; 15829 LNCS)
DOI
Classification of cells in microscopy images is an essential step in the diagnostic workflows for various medical conditions. These diagnostic processes benefit from emerging deep learning solutions, which make them more accessible, reliable, and scalable. However, their extensive deployment is hindered by limited generalizability and high computational demands of such architectures. We address this issue by introducing a lightweight, general-purpose hierarchical classification model based on Neural Cellular Automata (NCA). Our approach utilizes NCA to extract features at multiple resolutions, combining the advantages of NCA-based methods with those of convolutional architectures. We evaluate our model on six microscopy datasets from different modalities and demonstrate that it consistently outperforms existing NCA-based approaches. With significantly fewer parameters than conventional deep learning methods, our model is suitable for deployment in resource-constrained areas, such as remote clinics with limited computational infrastructure or mobile devices with lower computational capacities. Our results highlight the potential of NCA-based models as an effective, lightweight alternative for image classification, addressing critical barriers to the equitable distribution of automated diagnostic tools.
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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter General Purpose Solutions ; Image Classification ; Lightweight ; Microscopy Image Analysis ; Neural Cellular Automata ; Robustness
Sprache englisch
Veröffentlichungsjahr 2026
HGF-Berichtsjahr 2026
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel 29th International Conference on Information Processing in Medical Imaging, IPMI 2025
Konferzenzdatum 25-30 May 2025
Konferenzort Kos
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 15829 LNCS, Heft: , Seiten: 19-32 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Institut(e) Institute of AI for Health (AIH)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-540007-001
DOI 10.1007/978-3-031-96628-6_2
Scopus ID 105014495549
Erfassungsdatum 2025-10-22
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