FET-UNet: Merging CNN and transformer architectures for superior breast ultrasound image segmentation.
Journal:
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PMID:
40184647
Abstract
PURPOSE: Breast cancer remains a significant cause of mortality among women globally, highlighting the critical need for accurate diagnosis. Although Convolutional Neural Networks (CNNs) have shown effectiveness in segmenting breast ultrasound images, they often face challenges in capturing long-range dependencies, particularly for lesions with similar intensity distributions, irregular shapes, and blurred boundaries. To overcome these limitations, we introduce FET-UNet, a novel hybrid framework that integrates CNNs and Swin Transformers within a UNet-like architecture.