Deep learning for brain disorders: from data processing to disease treatment
In order to reach precision medicine and improve patients' quality of life, machine learning is
increasingly used in medicine. Brain disorders are often complex and heterogeneous, and …
increasingly used in medicine. Brain disorders are often complex and heterogeneous, and …
[HTML][HTML] A new end-to-end multi-dimensional CNN framework for land cover/land use change detection in multi-source remote sensing datasets
ST Seydi, M Hasanlou, M Amani - Remote Sensing, 2020 - mdpi.com
The diversity of change detection (CD) methods and the limitations in generalizing these
techniques using different types of remote sensing datasets over various study areas have …
techniques using different types of remote sensing datasets over various study areas have …
[HTML][HTML] Survey of methods and principles in three-dimensional reconstruction from two-dimensional medical images
M Sarmah, A Neelima, HR Singh - Visual computing for industry …, 2023 - Springer
Abstract Three-dimensional (3D) reconstruction of human organs has gained attention in
recent years due to advances in the Internet and graphics processing units. In the coming …
recent years due to advances in the Internet and graphics processing units. In the coming …
TransMRSR: transformer-based self-distilled generative prior for brain MRI super-resolution
Magnetic resonance images (MRI) acquired with low through-plane resolution compromise
time and cost. The poor resolution in one orientation is insufficient to meet the requirement of …
time and cost. The poor resolution in one orientation is insufficient to meet the requirement of …
An arbitrary scale super-resolution approach for 3d mr images via implicit neural representation
High Resolution (HR) medical images provide rich anatomical structure details to facilitate
early and accurate diagnosis. In magnetic resonance imaging (MRI), restricted by hardware …
early and accurate diagnosis. In magnetic resonance imaging (MRI), restricted by hardware …
A new structure for binary and multiple hyperspectral change detection based on spectral unmixing and convolutional neural network
ST Seydi, M Hasanlou - Measurement, 2021 - Elsevier
The earth is constantly being changed by natural events and human activities that constantly
threaten our environment. Therefore, accurate and timely monitoring of changes at the …
threaten our environment. Therefore, accurate and timely monitoring of changes at the …
Autoencoder-inspired convolutional network-based super-resolution method in MRI
Objective: To introduce an MRI in-plane resolution enhancement method that estimates
High-Resolution (HR) MRIs from Low-Resolution (LR) MRIs. Method & Materials: Previous …
High-Resolution (HR) MRIs from Low-Resolution (LR) MRIs. Method & Materials: Previous …
[HTML][HTML] Super-resolution reconstruction of brain magnetic resonance images via lightweight autoencoder
J Andrew, TSR Mhatesh, RD Sebastin… - Informatics in Medicine …, 2021 - Elsevier
Abstract Magnetic Resonance Imaging (MRI) is useful to provide detailed anatomical
information such as images of tissues and organs within the body that are vital for …
information such as images of tissues and organs within the body that are vital for …
Thermal simulation trained deep neural networks for fast and accurate prediction of thermal distribution and heat losses of building structures
In this study, state-of-the art deep neural networks to train and predict the heat transfer in
building structures were proposed. Today, many of studies analyze thermal energy …
building structures were proposed. Today, many of studies analyze thermal energy …
[HTML][HTML] Deep robust residual network for super-resolution of 2D fetal brain MRI
Spatial resolution is a key factor of quantitatively evaluating the quality of magnetic
resonance imagery (MRI). Super-resolution (SR) approaches can improve its spatial …
resonance imagery (MRI). Super-resolution (SR) approaches can improve its spatial …