Deep reinforcement learning (DRL) augments the reinforcement learning framework, which
learns a sequence of actions that maximizes the expected reward, with the representative …

Cardiovascular magnetic resonance (CMR) has become a key imaging modality in clinical
cardiology practice due to its unique capabilities for non-invasive imaging of the cardiac …

Artificial intelligence (AI) has experienced rapid growth over the past few years, moving from
the experimental to the implementation phase in various fields, including medicine …

Motivation Medical image analysis involves a series of tasks used to assist physicians in
qualitative and quantitative analyses of lesions or anatomical structures which can …

While minimally invasive surgery offers great benefits in terms of reduced patient trauma,
bleeding, as well as faster recovery time, it still presents surgeons with major ergonomic …

Background Preservation and improvement of heart and vessel health is the primary
motivation behind cardiovascular disease (CVD) research. Development of advanced …

Self-supervised learning aims to learn transferable representations from unlabeled data for
downstream tasks. Inspired by masked language modeling in natural language processing …

Reinforcement learning takes sequential decision-making approaches by learning the policy
through trial and error based on interaction with the environment. Combining deep learning …

Applying machine learning technologies, especially deep learning, into medical image
segmentation is being widely studied because of its state-of-the-art performance and results …

Statistical shape models (SSMs) have been widely employed in cardiac image
segmentation. However, in conditions that induce severe shape abnormality and …