Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …

The use of AI systems in healthcare for the early screening of diseases is of great clinical
importance. Deep learning has shown great promise in medical imaging, but the reliability …

The comprehensive integration of machine learning healthcare models within clinical
practice remains suboptimal, notwithstanding the proliferation of high-performing solutions …

Image-based simulation, the use of 3D images to calculate physical quantities, relies on
image segmentation for geometry creation. However, this process introduces image …

Ultrasound computed tomography (USCT) is a noninvasive biomedical imaging modality
that can reconstruct the speed of sound (SOS) distributions of biological tissues for disease …

Cell segmentation is a critical step in histology images analysis. Recently, Convolutional
Neural Network (CNN) has shown outstanding performance for various segmentation …

COVID-19 is a disease that spreads relatively quickly. So that many victims are infected by
this virus. There are various ways to diagnose the body's infection with the coronavirus. One …

Accurate colon segmentation on abdominal CT scans is crucial for various clinical
applications. In this work, we propose an accurate approach to colon segmentation from …

Abstract Evaluation of predictive deep learning (DL) models beyond conventional
performance metrics has become increasingly important for applications in sensitive …

In order to solve the problem that the traditional power grid fault diagnosis along the railway
requires a lot of manpower and material resources, this paper proposes a fault diagnosis …