Computed tomography (CT) is a widely used imaging technique in both medical and
industrial applications. However, accurate CT reconstruction requires complete projection …

Despite the ever-increasing interest in applying deep learning (DL) models to medical
imaging, the typical scarcity and imbalance of medical datasets can severely impact the …

Metallic implants can heavily attenuate x-rays in computed tomography (CT) scans, leading
to severe artifacts in reconstructed images, which significantly jeopardize image quality and …

Breast tumor segmentation is critical to the diagnosis and treatment of breast cancer. In
clinical breast cancer analysis, experts often examine multi-modal images since such …

Due to the presence of metallic implants, the imaging quality of computed tomography (CT)
would be heavily degraded. With the rapid development of deep learning, several neural …

Metal artifacts lead to CT imaging quality degradation. With the success of deep learning
(DL) in medical imaging, a number of DL-based supervised methods have been developed …

During the security check, the metal in the baggage engenders serious metal artifacts on the
Computed Tomography (CT) image. To reduce the effect of metal artifacts on the judgment …

Breast tumor segmentation plays a critical role in the diagnosis and treatment of breast
diseases. Current breast tumor segmentation methods are mainly deep learning (DL) based …

Introduction Many tools have been developed to reduce metal artefacts in computed
tomography (CT) images resulting from metallic prosthesis; however, their relative …

Metal artifacts commonly appear in computed tomography (CT) images of the patient body
with metal implants and can affect disease diagnosis. Known deep learning and traditional …