The renaissance of deep learning has provided promising solutions to various tasks. While
conventional deep learning models are constructed for a single specific task, multi-task deep …

Abstract Purpose Since Generative Adversarial Network (GAN) was introduced into the field
of deep learning in 2014, it has received extensive attention from academia and industry …

Recent advancements with deep generative models have proven significant potential in the
task of image synthesis, detection, segmentation, and classification. Segmenting the medical …

Multi-contrast MRI protocols increase the level of morphological information available for
diagnosis. Yet, the number and quality of contrasts are limited in practice by various factors …

Deep learning algorithms have recently been developed that utilize patient anatomy and
raw imaging information to predict radiation dose, as a means to increase treatment …

Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in
patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify …

Myocardial pathology segmentation (MyoPS) can be a prerequisite for the accurate
diagnosis and treatment planning of myocardial infarction. However, achieving this …

3D semi-supervised medical image segmentation is extremely essential in computer-aided
diagnosis, which can reduce the time-consuming task of performing annotation. The …

Obtaining manual labels is time-consuming and labor-intensive on cardiac image
sequences. Few-shot segmentation can utilize limited labels to learn new tasks. However, it …

In recent years, cardiovascular imaging examinations have experienced exponential growth
due to technological innovation, and this trend is consistent with the most recent chest pain …