  Histopathological image classification constitutes a pivotal task in
computer-aided diagnostics. The precise identification and categorization of
histopathological images are of paramount significance for early disease
detection and treatment. In the diagnostic process of pathologists, a
multi-tiered approach is typically employed to assess abnormalities in cell
regions at different magnifications. However, feature extraction is often
performed at a single granularity, overlooking the multi-granular
characteristics of cells. To address this issue, we propose the Fuzzy-guided
Multi-granularity Deep Neural Network (FMDNN). Inspired by the multi-granular
diagnostic approach of pathologists, we perform feature extraction on cell
structures at coarse, medium, and fine granularity, enabling the model to fully
harness the information in histopathological images. We incorporate the theory
of fuzzy logic to address the challenge of redundant key information arising
during multi-granular feature extraction. Cell features are described from
different perspectives using multiple fuzzy membership functions, which are
fused to create universal fuzzy features. A fuzzy-guided cross-attention module
guides universal fuzzy features toward multi-granular features. We propagate
these features through an encoder to all patch tokens, aiming to achieve
enhanced classification accuracy and robustness. In experiments on multiple
public datasets, our model exhibits a significant improvement in accuracy over
commonly used classification methods for histopathological image classification
and shows commendable interpretability.
