Frequency-domain multi-scale hybrid attention for pathological image classification

Pathological image classification is critical for early cancer diagnosis and precise subtyping. However, pathological images exhibit significant heterogeneity and complex textures. Existing methods often fail to fully exploit local details, while frequency-domain approaches lack effective inter-subband interaction, hindering the fusion of global context and local fine-grained information. To address this, this study propose a Multi-scale Frequency-domain Hybrid Attention mechanism (MFHA). MFHA uses wavelet transform to decompose images into low-frequency subbands (global structure) and high-frequency subbands (microscopic details). By integrating multi-scale convolutions with subband fusion, it enhances high-frequency feature representation, enabling effective joint modeling of global structure and local texture. This study further introduce a spatial attention module with cosine similarity and multi-dimensional statistics to improve feature robustness and discriminability. Experiments show that our method outperforms baselines on multiple pathological datasets, with accuracy gains of 1.97% and 2.71%. This work provides a novel perspective for pathological image feature co-modeling, boosting classification accuracy and robustness. The code is available at https://github.com/AnaStartz/WSI-processing-framework.