A review on joint carotid intima-media thickness and plaque area measurement in ultrasound for cardiovascular/stroke risk monitoring: artificial intelligence framework
Cardiovascular diseases (CVDs) are the top ten leading causes of death worldwide.
Atherosclerosis disease in the arteries is the main cause of the CVD, leading to myocardial …
Atherosclerosis disease in the arteries is the main cause of the CVD, leading to myocardial …
Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images
History shows that the infectious disease (COVID-19) can stun the world quickly, causing
massive losses to health, resulting in a profound impact on the lives of billions of people …
massive losses to health, resulting in a profound impact on the lives of billions of people …
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 …
ST-unet: Swin transformer boosted U-net with cross-layer feature enhancement for medical image segmentation
J Zhang, Q Qin, Q Ye, T Ruan - Computers in Biology and Medicine, 2023 - Elsevier
Medical image segmentation is an essential task in clinical diagnosis and case analysis.
Most of the existing methods are based on U-shaped convolutional neural networks (CNNs) …
Most of the existing methods are based on U-shaped convolutional neural networks (CNNs) …
A survey on shape-constraint deep learning for medical image segmentation
S Bohlender, I Oksuz… - IEEE Reviews in …, 2021 - ieeexplore.ieee.org
Since the advent of U-Net, fully convolutional deep neural networks and its many variants
have completely changed the modern landscape of deep-learning based medical image …
have completely changed the modern landscape of deep-learning based medical image …
[BOOK][B] Deep learning for medical image analysis
Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for
academic and industry researchers and graduate students taking courses on machine …
academic and industry researchers and graduate students taking courses on machine …
[PDF][PDF] Multi-classification of Alzheimer disease on magnetic resonance images (MRI) using deep convolutional neural network (DCNN) approaches
Alzheimer's disease (AD) is the most popular cause of dementia. Dementia refers to a
continuous decline in mental ability. The developmental stages of this neuropsychiatric …
continuous decline in mental ability. The developmental stages of this neuropsychiatric …
Unsupervised moving object detection via contextual information separation
We propose an adversarial contextual model for detecting moving objects in images. A deep
neural network is trained to predict the optical flow in a region using information from …
neural network is trained to predict the optical flow in a region using information from …
An adaptive differential evolution algorithm to optimal multi-level thresholding for MRI brain image segmentation
O Tarkhaneh, H Shen - Expert Systems with Applications, 2019 - Elsevier
Segmentation is an important method for MRI medical image analysis as it can provide the
radiologists with noninvasive information about a patient that is crucial to the diagnostic …
radiologists with noninvasive information about a patient that is crucial to the diagnostic …
Capsules for biomedical image segmentation
Our work expands the use of capsule networks to the task of object segmentation for the first
time in the literature. This is made possible via the introduction of locally-constrained routing …
time in the literature. This is made possible via the introduction of locally-constrained routing …