TY  - EJOU
AU  - Sajid, Muhammad Zaheer 
AU  - Hamid, Muhammad Fareed 
AU  - Khan, Nauman Ali 
AU  - Qureshi, Imran 

TI  - WaSA-Net: Wavelet-Guided Tokenization and Dynamic Sparse Attention for Histopathology Image Classification
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2026
VL  - 148
IS  - 1
SN  - 1526-1506

AB  - 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.
KW  - Digital pathology; wavelet transform; sparse attention; frequency-guided tokenization; telepathology; computational pathology; deep learning

DO  - 10.32604/cmes.2026.084724