Large language models (LLM) not only empower multiple language tasks but...
Deep learning (DL) has been extensively researched in the field of compu...
This paper studies 3D low-dose computed tomography (CT) imaging. Althoug...
Deep learning has been successfully applied to low-dose CT (LDCT) image
...
The presence of high-density objects such as metal implants and dental
f...
Minimum redundancy among different elements of an embedding in a latent ...
Self-supervised representation learning maps high-dimensional data into ...
Early detection of lung nodules with computed tomography (CT) is critica...
Although radiographs are the most frequently used worldwide due to their...
Digital breast tomosynthesis (DBT) exams should utilize the lowest possi...
X-ray imaging is the most popular medical imaging technology. While x-ra...
The phase function is a key element of a light propagation model for Mon...
By the ALARA (As Low As Reasonably Achievable) principle, ultra-low-dose...
The extensive use of medical CT has raised a public concern over the
rad...
Deep learning has shown great promise for CT image reconstruction, in
pa...
This paper presents SPICE, a Semantic Pseudo-labeling framework for Imag...
We propose a Noise Entangled GAN (NE-GAN) for simulating low-dose comput...
The key idea behind denoising methods is to perform a mean/averaging
ope...
Recently proposed neural architecture search (NAS) algorithms adopt neur...
Deep neural network based methods have achieved promising results for CT...
Neural architecture search (NAS) is a promising method for automatically...
Deep clustering has achieved state-of-the-art results via joint
represen...
Temporal action detection is a fundamental yet challenging task in video...
Pixel-level annotation demands expensive human efforts and limits the
pe...