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Imaging modalities to diagnose carotid artery stenosis: progress and prospect
In the past few decades, imaging has been developed to a high level of sophistication.
Improvements from one-dimension (1D) to 2D images, and from 2D images to 3D models …
Improvements from one-dimension (1D) to 2D images, and from 2D images to 3D models …
Breast cancer detection using extreme learning machine based on feature fusion with CNN deep features
A computer-aided diagnosis (CAD) system based on mammograms enables early breast
cancer detection, diagnosis, and treatment. However, the accuracy of the existing CAD …
cancer detection, diagnosis, and treatment. However, the accuracy of the existing CAD …
Carotid wall longitudinal motion in ultrasound imaging: an expert consensus review
Motion extracted from the carotid artery wall provides unique information for vascular health
evaluation. Carotid artery longitudinal wall motion corresponds to the multiphasic arterial …
evaluation. Carotid artery longitudinal wall motion corresponds to the multiphasic arterial …
Clinical interpretable deep learning model for glaucoma diagnosis
Despite the potential to revolutionise disease diagnosis by performing data-driven
classification, clinical interpretability of ConvNet remains challenging. In this paper, a novel …
classification, clinical interpretability of ConvNet remains challenging. In this paper, a novel …
Learning physical properties in complex visual scenes: An intelligent machine for perceiving blood flow dynamics from static CT angiography imaging
Humans perceive physical properties such as motion and elastic force by observing objects
in visual scenes. Recent research has proven that computers are capable of inferring …
in visual scenes. Recent research has proven that computers are capable of inferring …
Learning tree-structured representation for 3D coronary artery segmentation
Extensive research has been devoted to the segmentation of the coronary artery. However,
owing to its complex anatomical structure, it is extremely challenging to automatically …
owing to its complex anatomical structure, it is extremely challenging to automatically …
A deep learning-based approach for automatic segmentation and quantification of the left ventricle from cardiac cine MR images
Cardiac MRI has been widely used for noninvasive assessment of cardiac anatomy and
function as well as heart diagnosis. The estimation of physiological heart parameters for …
function as well as heart diagnosis. The estimation of physiological heart parameters for …
Privileged modality distillation for vessel border detection in intracoronary imaging
Intracoronary imaging is a crucial imaging technology in coronary disease diagnosis as it
visualizes the internal tissue morphologies of coronary arteries. Vessel border detection in …
visualizes the internal tissue morphologies of coronary arteries. Vessel border detection in …
Multi-level semantic adaptation for few-shot segmentation on cardiac image sequences
Obtaining manual labels is time-consuming and labor-intensive on cardiac image
sequences. Few-shot segmentation can utilize limited labels to learn new tasks. However, it …
sequences. Few-shot segmentation can utilize limited labels to learn new tasks. However, it …
Left ventricle automatic segmentation in cardiac MRI using a combined CNN and U-net approach
Cardiovascular diseases can be effectively prevented from worsening through early
diagnosis. To this end, various methods have been proposed to detect the disease source …
diagnosis. To this end, various methods have been proposed to detect the disease source …