As an emerging biomedical image processing technology, medical image segmentation has
made great contributions to sustainable medical care. Now it has become an important …

Deep learning has been widely used for medical image segmentation and a large number of
papers has been presented recording the success of deep learning in the field. A …

The emergence of deep learning has considerably advanced the state-of-the-art in cardiac
magnetic resonance (CMR) segmentation. Many techniques have been proposed over the …

Navigating a large swarm of micro-/nanorobots is critical for potential targeted
delivery/therapy applications owing to the limited volume/function of a single microrobot, and …

With the advent of advancements in deep learning approaches, such as deep convolution
neural network, residual neural network, adversarial network; U-Net architectures are most …

Abstract Cardiac digital twins (Cardiac Digital Twin (CDT) s) of human electrophysiology
(Electrophysiology (EP)) are digital replicas of patient hearts derived from clinical data that …

Research into artificial intelligence (AI) has made tremendous progress over the past
decade. In particular, the AI-powered analysis of images and signals has reached human …

We present a critical assessment of the role of transfer learning in training fully convolutional
networks (FCNs) for medical image segmentation. We first show that although transfer …

Within the vast expanse of computerized language processing, a revolutionary entity known
as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to …

Objectives This study sought to examine if fully automated measurements of global
longitudinal strain (GLS) using a novel motion estimation technology based on deep …