Browsing by Author "Açıcı, Koray"

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  • Article
    Learning Continuous Activation Fields from Microscopic SEM Images of Lycoperdioid Fungi via CNN-Guided Neural Operator Modeling
    (Nature Portfolio, 2026) Ediş, Gülce; Ekinci, Fatih; Kumru, Eda; Açıcı, Koray; Akata, Ilgaz; Demir, Özge; Aydın, Ayhan
    Accurate interpretation of microscopic morphological features is critical for resolving taxonomic boundaries in morphologically similar fungal taxa, yet conventional image analysis approaches largely rely on discrete classification or pixel-level segmentation, which fail to capture the inherently continuous nature of biological microstructures. In this work, a continuous spatial representation learning framework is introduced for the analysis of scanning electron microscopy (SEM) images of lycoperdioid fungal spores by integrating convolutional neural network (CNN)-based local feature encoding with a coordinate-aware neural operator architecture. Microscopic images are reformulated as functions defined over a two-dimensional spatial domain, and a Deep Operator Network (DeepONet) is employed to learn mappings from image functions to continuous activation fields under weak supervision. The proposed framework was evaluated on high-resolution SEM images from five lycoperdioid species, acquired under standardized imaging conditions. Quantitative analyses within the evaluated model configuration indicate that the operator-learning-based approach achieves a 28-35% reduction in mean squared error relative to proxy-based activation references and an 18-22% improvement over patch-level CNN representations without operator coupling. Residual norm and line-profile analyses further reveal a similar to 30% suppression of high-frequency spatial fluctuations, particularly in boundary-dense regions, indicating enhanced spatial regularization. Explainability analysis based on continuous activation fields shows that over 70% of high-response regions consistently align with diagnostically relevant micromorphological features, while same-class activation variance is reduced by 20-25%, confirming improved representational stability without over-smoothing. By moving beyond discrete decision-making toward operator-based continuous modeling, this study establishes a scalable and interpretable framework for microscopic SEM image analysis. The proposed methodology provides a transferable foundation for explainable artificial intelligence applications across fungal taxonomy, biomedical imaging, and other domains requiring high-resolution spatial interpretation of complex microstructures.