A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
SK Zhou, H Greenspan, C Davatzikos… - Proceedings of the …, 2021 - ieeexplore.ieee.org
Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …
and has achieved remarkable success in many medical imaging applications, thereby …
Deep learning for cardiac image segmentation: a review
Deep learning has become the most widely used approach for cardiac image segmentation
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …
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 …
Automated detection and forecasting of covid-19 using deep learning techniques: A review
A Shoeibi, M Khodatars, M Jafari, N Ghassemi… - Neurocomputing, 2024 - Elsevier
Abstract In March 2020, the World Health Organization (WHO) declared COVID-19 a global
epidemic, caused by the SARS-CoV-2 virus. Initially, COVID-19 was diagnosed using real …
epidemic, caused by the SARS-CoV-2 virus. Initially, COVID-19 was diagnosed using real …
Weakly-supervised disentanglement without compromises
Intelligent agents should be able to learn useful representations by observing changes in
their environment. We model such observations as pairs of non-iid images sharing at least …
their environment. We model such observations as pairs of non-iid images sharing at least …
Causal knowledge fusion for 3D cross-modality cardiac image segmentation
Abstract Three-dimensional (3D) cross-modality cardiac image segmentation is critical for
cardiac disease diagnosis and treatment. However, it confronts the challenge of modality …
cardiac disease diagnosis and treatment. However, it confronts the challenge of modality …
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 …
[HTML][HTML] Learning disentangled representations in the imaging domain
Disentangled representation learning has been proposed as an approach to learning
general representations even in the absence of, or with limited, supervision. A good general …
general representations even in the absence of, or with limited, supervision. A good general …
[HTML][HTML] Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory
In magnetic resonance (MR) imaging, a lack of standardization in acquisition often causes
pulse sequence-based contrast variations in MR images from site to site, which impedes …
pulse sequence-based contrast variations in MR images from site to site, which impedes …
Unsupervised domain adaptation for medical image segmentation by disentanglement learning and self-training
Unsupervised domain adaption (UDA), which aims to enhance the segmentation
performance of deep models on unlabeled data, has recently drawn much attention. In this …
performance of deep models on unlabeled data, has recently drawn much attention. In this …