Cross-Center Stroke Evaluation using Carotid Ultrasound Patterns via Scale-Adaptive Frequency-Enhanced Neural Network

Medical imaging datasets vary across institutions due to differences in scanner types, posing challenges for model generalization. To address the issue, this study proposed a scale-adaptive frequency-enhanced neural network (SAFE-Net), which leveraged a DWT-ViT encoder to incorporate frequency-domain features into a transformer-based architecture for cross-center stroke evaluation. The discrete wavelet transform (DWT) decomposed carotid color Doppler (CCD) images into multiresolution frequency sub-bands. The extracted high-resolution (Xh) and low-resolution (Xl) features are concatenated to enhance both local and global feature extraction. Experimental results demonstrated that SAFE-Net achieves the highest accuracy, with 92.5% accuracy on Institution A and 93.75% on Institution B, outperforming other models in stroke evaluation. SAFE-Net maintains cross-center generalization, with an accuracy of 90.73% when trained on Institution A and tested on Institution B, and 89.16% in the reverse setting. Also, the model required 25% fewer parameters and 2.0× fewer FLOPs than conventional models. These findings underscored the effectiveness of SAFE-Net across diverse medical imaging datasets.