Objective: For several years, computer technology has been utilized to diagnose lung
nodules. When compared to traditional machine learning methods for image processing …

Deep learning has been demonstrated effective in many neuroimaging applications.
However, in many scenarios, the number of imaging sequences capturing information …

This study aimed to build progressively operating deep learning models that could detect
meniscus injuries, anterior cruciate ligament (ACL) tears and knee abnormalities in …

U-Net is the most cited and widely-used deep learning model for biomedical image
segmentation. In this paper, we propose a new enhanced version of a ubiquitous U-Net …

Abstract The Internet of Things (IoT), big data, and artificial intelligence (AI) are all key
technologies that influence the formation and implementation of digital medical services …

Background: Breast mass is one of the main symptoms of breast cancer. Effective and
accurate detection of breast masses at an early stage would be of great value for clinical …

Mutated cells may constitute a source of cancer. As an effective approach to quantifying the
extent of cancer, cell image segmentation is of particular importance for understanding the …

Reliable analysis of intracellular dynamic processes in time-lapse fluorescence microscopy
images requires complete and accurate tracking of all small particles in all time frames of the …

Purpose To evaluate the image quality (IQ) of synthesized two-dimensional (s2D) and
tomographic layer (TL) mammographic images in comparison to the 2D digital …

Arterial spin-labeled (ASL) perfusion MRI remains the only non-invasive, radiation-free
method for quantifying regional tissue perfusion. ASL MRI computes perfusion signals from …