WaSA-Net: Wavelet-Guided Tokenization and Dynamic Sparse Attention for Histopathology Image Classification

Digital pathology is rapidly transforming histopathological diagnosis, yet many existing deep learning models treat all spatial regions uniformly and do not exploit the multi-frequency structure of tissue, which limits both diagnostic accuracy and computational efficiency. This paper proposes WaSA-Net, an end-to-end architecture that integrates three complementary modules for histopathological image analysis. First, the Wavelet-Guided Tokenization (WGT) module decomposes input images into frequency-aware representations using learnable wavelet-like filters, so that both global tissue structures and fine-grained cellular patterns are exposed to attention from the first layer. Second, the Dynamic Sparse Attention with Pathology Priors (DSA-PP) module adaptively selects diagnostically informative tokens through a lightweight gating mechanism and incorporates learnable pathology prior tokens that embed domain-specific inductive biases, reducing attention complexity while preserving critical contextual information. Third, the Cross-Frequency Feature Pyramid Fusion (CFFPF) module performs bidirectional cross-attention across frequency bands and applies adaptive per-sample frequency weighting to identify the most discriminative frequency components for each tissue type. The proposed architecture is evaluated on three widely used histopathology benchmarks: PatchCamelyon for metastasis detection, PathMNIST for multi-class colorectal tissue classification, and BreakHis for breast cancer diagnosis. WaSA-Net achieves strong performance with only 4.8M parameters, reaching 95.91% accuracy (AUC 0.9981) on PathMNIST, 93.47% accuracy (AUC 0.9812) on PatchCamelyon, and 96.72% accuracy (AUC 0.9923) on BreakHis. Despite its compact design, WaSA-Net matches or surpasses larger models while requiring no external pre-training data. These results indicate that frequency-aware representations and dynamic sparse attention can improve both efficiency and diagnostic performance in digital pathology.