HyFormer-Net: A Synergistic CNN-Transformer with Interpretable Multi-Scale Fusion for Breast Lesion Segmentation and Classification in Ultrasound Images

HyFormer-Net is a groundbreaking hybrid model that combines CNN and Transformer architectures to improve breast lesion segmentation and classification in ultrasound images. This innovation addresses significant challenges in breast cancer diagnosis, such as speckle noise and indistinct boundaries, which have hindered the effectiveness of existing deep learning methods. By enabling simultaneous segmentation and classification, HyFormer-Net not only enhances diagnostic accuracy but also promotes clinical adoption of advanced imaging techniques, making it a vital development in the fight against breast cancer.