Hybrid attention-driven mixture of experts network for automated breast cancer grading from histopathological images

Breast cancer grading based on histopathological images plays a crucial role in determining tumor progression and guiding therapeutic decisions. However, manual diagnosis remains time-consuming and prone to inter-observer variability. To address this challenge, we propose a novel Hybrid Attention-Driven Mixture of Experts (HAMoE) architecture that integrates a custom five-block convolutional neural network backbone with Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) attention mechanisms within an advanced multi-head Mixture-of-Experts framework. The proposed model employs generalized focal loss to mitigate class imbalance and emphasize hard samples, alongside cosine annealing learning rate scheduling to stabilize convergence. Furthermore, Test-Time Augmentation (TTA) and Monte-Carlo Dropout inference are utilized to improve predictive reliability and uncertainty estimation. Experiments conducted on a clinically collected breast cancer histopathological dataset demonstrate that the proposed framework achieves superior grading performance compared with conventional deep learning architectures. The model attains up to 97.83% validation accuracy and exhibits robust generalization across different tumor grades. These findings highlight the potential of the proposed HAMoE framework as an effective computer-aided diagnostic tool for automated breast cancer grading and more consistent histopathological assessment in clinical practice.