U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …

Background Due to the advancement of medical imaging and computer technology,
machine intelligence to analyze clinical image data increases the probability of disease …

Background Lung cancer is a worldwide high-risk disease, and lung nodules are the main
manifestation of early lung cancer. Automatic detection of lung nodules reduces the …

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of
mortality globally. At early stages, CVDs appear with minor symptoms and progressively get …

In cardiology, ultrasound is often used to diagnose heart disease associated with myocardial
infarction. This study aims to develop robust segmentation techniques for segmenting the left …

Background and objective Deep learning, a novel approach and subset of machine
learning, has drawn a growing amount of attention from computer vision researchers in …

Purpose Using computer-assisted means to process a large amount of heart image data in
order to speed up the diagnosis efficiency and accuracy of medical doctors has become a …

Objective This paper aims to improve U-Net for more accurate segmentation of irregular
intracranial hemorrhage lesions in CT images. Methods The residual octave convolution …

Background and Objective: Cardiovascular disease is a high-fatality health issue. Accurate
measurement of cardiovascular function depends on precise segmentation of physiological …

This study presents a data-driven finite element-machine learning surrogate model for
predicting the end-to-end full-field stress distribution and stress concentration around an …