In the field of medical image analysis within deep learning (DL), the importance of
employing advanced DL techniques cannot be overstated. DL has achieved impressive …

Tomographic image reconstruction with deep learning is an emerging field of applied
artificial intelligence. Reducing radiation dose with sparse views' reconstruction is a …

Recently, vision-based detection (VD) technology has been well-developed, and its general-
purpose object detection algorithms have been applied in various scenes. VD can be …

Low-dose computed tomography (CT) images suffer from noise and artifacts due to photon
starvation and electronic noise. Recently, some works have attempted to use diffusion …

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

Sparse-view computed tomography (CT)---using a small number of projections for
tomographic reconstruction---enables much lower radiation dose to patients and …

Computed Tomography (CT) has become a mainstream imaging tool in medical diagnosis.
However, the issue of increased cancer risk due to radiation exposure has raised public …

This paper studies 3D low-dose computed tomography (CT) imaging. Although various deep
learning methods were developed in this context, typically they focus on 2D images and …

Digital technologies present unrivaled opportunities to improve healthcare services
worldwide. Medical devices and hospitals are now using innovative techniques to diagnose …

In the existing deep learning-based watermarking models, extracted image features for
fusing with watermark are not abundant enough and more critically, and essential features …