A review of deep-learning-based medical image segmentation methods
As an emerging biomedical image processing technology, medical image segmentation has
made great contributions to sustainable medical care. Now it has become an important …
made great contributions to sustainable medical care. Now it has become an important …
Medical image segmentation using deep learning: A survey
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 …
papers has been presented recording the success of deep learning in the field. A …
Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M&Ms challenge
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 …
magnetic resonance (CMR) segmentation. Many techniques have been proposed over the …
Autonomous environment-adaptive microrobot swarm navigation enabled by deep learning-based real-time distribution planning
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 …
delivery/therapy applications owing to the limited volume/function of a single microrobot, and …
Modality specific U-Net variants for biomedical image segmentation: a survey
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 …
neural network, residual neural network, adversarial network; U-Net architectures are most …
[HTML][HTML] A framework for the generation of digital twins of cardiac electrophysiology from clinical 12-leads ECGs
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 …
(Electrophysiology (EP)) are digital replicas of patient hearts derived from clinical data that …
Applications of artificial intelligence in cardiovascular imaging
M Sermesant, H Delingette, H Cochet, P Jaïs… - Nature Reviews …, 2021 - nature.com
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 …
decade. In particular, the AI-powered analysis of images and signals has reached human …
Transfer learning in medical image segmentation: New insights from analysis of the dynamics of model parameters and learned representations
D Karimi, SK Warfield, A Gholipour - Artificial intelligence in medicine, 2021 - Elsevier
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 …
networks (FCNs) for medical image segmentation. We first show that although transfer …
Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects
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 …
as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to …
Artificial intelligence for automatic measurement of left ventricular strain in echocardiography
Objectives This study sought to examine if fully automated measurements of global
longitudinal strain (GLS) using a novel motion estimation technology based on deep …
longitudinal strain (GLS) using a novel motion estimation technology based on deep …