In medical imaging, segmentation plays a vital role towards the interpretation of X-ray
images where salient features are extracted with the help of image segmentation. Without …

Most artificial intelligence (AI) studies have focused primarily on adult imaging, with less
attention to the unique aspects of pediatric imaging. The objectives of this study were to (1) …

Photoacoustic imaging is an emerging biomedical imaging technique that combines optical
contrast and ultrasound resolution to create unprecedented light absorption contrast in deep …

Background and Motivation: COVID-19 has resulted in a massive loss of life during the last
two years. The current imaging-based diagnostic methods for COVID-19 detection in …

Transfer learning from supervised ImageNet models has been frequently used in medical
image analysis. Yet, no large-scale evaluation has been conducted to benchmark the …

Large variations in anatomical shape and size, too much overlap between anatomical
structures, and inconsistent anatomical shapes make accurate lung segmentation in chest x …

Automatic segmentation of the lungs in Chest X-ray images (CXRs) is a key step in the
screening and diagnosis of related diseases. There are many opacities in the lungs in the …

To detect and diagnosis the lungs related diseases, a Chest X-Ray (CXR) is the major tool
used by the physician. Automated organ segmentation contributes to a crucial part of …

We propose a deep learning framework to improve segmentation accuracy of the lung
region in Chest X-Ray (CXR) images. The proposed methodology implements a “divide and …

Despite the progress in utilizing deep learning to automate chest radiograph interpretation
and disease diagnosis tasks, change between sequential Chest X-rays (CXRs) has received …