Generative AI for brain image computing and brain network computing: a review
Recent years have witnessed a significant advancement in brain imaging techniques that
offer a non-invasive approach to map** the structure and function of the brain …
offer a non-invasive approach to map** the structure and function of the brain …
[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] Variational encoding approach for interpretable assessment of remaining useful life estimation
A new method for evaluating aircraft engine monitoring data is proposed. Commonly,
prognostics and health management systems use knowledge of the degradation processes …
prognostics and health management systems use knowledge of the degradation processes …
Recent advances in variational autoencoders with representation learning for biomedical informatics: A survey
Variational autoencoders (VAEs) are deep latent space generative models that have been
immensely successful in multiple exciting applications in biomedical informatics such as …
immensely successful in multiple exciting applications in biomedical informatics such as …
Conditional GAN with 3D discriminator for MRI generation of Alzheimer's disease progression
Many studies aim to predict the degree of deformation on affected brain regions as
Alzheimer's disease (AD) progresses. However, those studies have been often limited since …
Alzheimer's disease (AD) progresses. However, those studies have been often limited since …
Collagen fiber centerline tracking in fibrotic tissue via deep neural networks with variational autoencoder-based synthetic training data generation
The role of fibrillar collagen in the tissue microenvironment is critical in disease contexts
ranging from cancers to chronic inflammations, as evidenced by many studies. Quantifying …
ranging from cancers to chronic inflammations, as evidenced by many studies. Quantifying …
Self-supervised learning of neighborhood embedding for longitudinal MRI
In recent years, several deep learning models recommend first to represent Magnetic
Resonance Imaging (MRI) as latent features before performing a downstream task of interest …
Resonance Imaging (MRI) as latent features before performing a downstream task of interest …
Longitudinal self-supervised learning
Abstract Machine learning analysis of longitudinal neuroimaging data is typically based on
supervised learning, which requires large number of ground-truth labels to be informative …
supervised learning, which requires large number of ground-truth labels to be informative …
Laser-based optical wireless communications for internet of things (IoT) application
Internet of Things (IoT) is enabled by the integration of communication and sensor systems
that are used to collect important information from objects around the world. In this article, we …
that are used to collect important information from objects around the world. In this article, we …
Conditional GAN with an attention-based generator and a 3D discriminator for 3D medical image generation
Abstract Conditional Generative Adversarial Networks (cGANs) are a set of methods able to
synthesize images that match a given condition. However, existing models designed for …
synthesize images that match a given condition. However, existing models designed for …