WiTUnet: A U-Shaped Architecture Integrating CNN and Transformer for Improved Feature Alignment and Local Information Fusion

arXiv:2404.09533v1 Announce Type: cross
Abstract: Low-dose computed tomography (LDCT) has become the technology of choice for diagnostic medical imaging, given its lower radiation dose compared to standard CT, despite increasing image noise and potentially affecting diagnostic accuracy. To address this, advanced deep learning-based LDCT denoising algorithms have been developed, primarily using Convolutional Neural Networks (CNNs) or Transformer Networks with the Unet architecture. This architecture enhances image detail by integrating feature maps from the encoder and decoder via skip connections. However, …

abstract accuracy advanced alignment architecture arxiv become cnn cs.ai cs.cv cs.lg deep learning diagnostic feature fusion image imaging information low medical medical imaging noise standard technology transformer type