U-shaped networks have become prevalent in various medical image tasks such as
segmentation, and restoration. However, most existing U-shaped networks rely on …

Medical image analysis forms the basis of image-guided surgery (IGS) and many of its
fundamental tasks. Driven by the growing number of medical imaging modalities, the …

To prevent patients from being exposed to excess of radiation in CT imaging, the most
common solution is to decrease the radiation dose by reducing the X-ray, and thus the …

Low-dose computed tomography (LDCT) can significantly reduce the damage of X-ray to the
human body, but the reduction of CT dose will produce images with severe noise and …

In clinical applications, multi-dose scan protocols will cause the noise levels of computed
tomography (CT) images to fluctuate widely. The popular low-dose CT (LDCT) denoising …

Low-dose computed tomography (LDCT) reduces the damage caused by ionizing radiation
by reducing the X-ray dose. However, CT images reconstructed from low-dose X-rays …

U-shaped networks are widely used in various medical image tasks, such as segmentation,
restoration and reconstruction, but most of them usually rely on centralized learning and thus …

Objective. Deep neural network (DNN) based methods have shown promising performances
for low-dose computed tomography (LDCT) imaging. However, most of the DNN-based …

X-ray computed tomography (CT) plays a crucial role in modern medical imaging for its non-
invasive acquisition of anatomical information about the human body. However, its inherent …

Deep learning (DL) algorithms have achieved unprecedented success in low-dose CT
(LDCT) imaging and are expected to be a new generation of CT reconstruction technology …