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

Neural Cellular Automata for Weakly Supervised Segmentation of White Blood Cells.

In: Efficient Medical Artificial Intelligence. Berlin [u.a.]: Springer, 2026. 289 - 298 (Lect. Notes Comput. Sc. ; 16318 LNCS)
DOI
The detection and segmentation of white blood cells in blood smear images is a key step in medical diagnostics, supporting various downstream tasks such as automated blood cell counting, morphological analysis, cell classification, and disease diagnosis and monitoring. Training robust and accurate models requires large amounts of labeled data, which is both time-consuming and expensive to acquire. In this work, we propose a novel approach for weakly supervised segmentation using neural cellular automata (NCA-WSS). By leveraging the feature maps generated by NCA during classification, we can extract segmentation masks without the need for retraining with segmentation labels. We evaluate our method on three white blood cell microscopy datasets and demonstrate that NCA-WSS significantly outperforms existing weakly supervised approaches. Our work illustrates the potential of NCA for both classification and segmentation in a weakly supervised framework, providing a scalable and efficient solution for medical image analysis.
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Publication type Article: Periodical or book chapter
Keywords Neural Cellular Automata ; Weakly Supervised Segmentation ; White Blood Cells
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Book Volume Title Efficient Medical Artificial Intelligence
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 16318 LNCS, Issue: , Pages: 289 - 298 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Institute(s) Institute of AI for Health (AIH)