Methods for image denoising using convolutional neural network: a review
AE Ilesanmi, TO Ilesanmi - Complex & Intelligent Systems, 2021 - Springer
Image denoising faces significant challenges, arising from the sources of noise. Specifically,
Gaussian, impulse, salt, pepper, and speckle noise are complicated sources of noise in …
Gaussian, impulse, salt, pepper, and speckle noise are complicated sources of noise in …
Understanding deep learning techniques for image segmentation
The machine learning community has been overwhelmed by a plethora of deep learning--
based approaches. Many challenging computer vision tasks, such as detection, localization …
based approaches. Many challenging computer vision tasks, such as detection, localization …
What do we need to build explainable AI systems for the medical domain?
Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate
impressive practical success in many different application domains, eg in autonomous …
impressive practical success in many different application domains, eg in autonomous …
Deep learning for undersampled MRI reconstruction
This paper presents a deep learning method for faster magnetic resonance imaging (MRI)
by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for …
by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for …
The importance of resource awareness in artificial intelligence for healthcare
Artificial intelligence and machine learning (AI/ML) models have been adopted in a wide
range of healthcare applications, from medical image computing and analysis to continuous …
range of healthcare applications, from medical image computing and analysis to continuous …
[HTML][HTML] Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions
Removing the bias and variance of multicentre data has always been a challenge in large
scale digital healthcare studies, which requires the ability to integrate clinical features …
scale digital healthcare studies, which requires the ability to integrate clinical features …
CNN-DMRI: a convolutional neural network for denoising of magnetic resonance images
Abstract Magnetic Resonance Images (MRI) are often contaminated by rician noise at the
acquisition time. This type of noise typically deteriorates the performance of disease …
acquisition time. This type of noise typically deteriorates the performance of disease …
Boundary delineation of MRI images for lumbar spinal stenosis detection through semantic segmentation using deep neural networks
We propose a methodology to aid clinicians in performing lumbar spinal stenosis detection
through semantic segmentation and delineation of magnetic resonance imaging (MRI) …
through semantic segmentation and delineation of magnetic resonance imaging (MRI) …
Quantitative texture analysis of brain white matter lesions derived from T2-weighted MR images in MS patients with clinically isolated syndrome
Introduction This study investigates the application of texture analysis methods on brain T2-
white matter lesions detected with magnetic resonance imaging (MRI) for the prognosis of …
white matter lesions detected with magnetic resonance imaging (MRI) for the prognosis of …
An intensity-texture model based level set method for image segmentation
In this paper, a novel level set segmentation model integrating the intensity and texture
terms is proposed to segment complicated two-phase nature images. Firstly, an intensity …
terms is proposed to segment complicated two-phase nature images. Firstly, an intensity …