[HTML][HTML] 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 …
Boundary-weighted domain adaptive neural network for prostate MR image segmentation
Accurate segmentation of the prostate from magnetic resonance (MR) images provides
useful information for prostate cancer diagnosis and treatment. However, automated …
useful information for prostate cancer diagnosis and treatment. However, automated …
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 …
3D PBV-Net: an automated prostate MRI data segmentation method
Prostate cancer is one of the most common deadly diseases in men worldwide, which is
seriously affecting people's life and health. Reliable and automated segmentation of the …
seriously affecting people's life and health. Reliable and automated segmentation of the …
An open IoHT-based deep learning framework for online medical image recognition
CMJM Dourado, SPP da Silva… - IEEE Journal on …, 2020 - ieeexplore.ieee.org
Systems developed to work with computational intelligence have become very efficient, and
in some cases obtain more accurate results than evaluations by humans. Hence, this work …
in some cases obtain more accurate results than evaluations by humans. Hence, this work …
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 …
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 …
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 …