Deep learning has received extensive research interest in developing new medical image
processing algorithms, and deep learning based models have been remarkably successful …

Deep learning-based image segmentation is by now firmly established as a robust tool in
image segmentation. It has been widely used to separate homogeneous areas as the first …

Convolutional neural networks, are one of the most representative deep learning models.
CNNs were extensively used in many aspects of medical image analysis, allowing for great …

This paper reviews state-of-the-art research solutions across the spectrum of medical
imaging informatics, discusses clinical translation, and provides future directions for …

Recent advances in transformer-based models have drawn attention to exploring these
techniques in medical image segmentation, especially in conjunction with the U-Net model …

Background Magnetic resonance imaging (MRI) of the knee is the preferred method for
diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject …

Traditional neuroimage analysis pipelines involve computationally intensive, time-
consuming optimization steps, and thus, do not scale well to large cohort studies with …

In this study, we propose a three-dimensional Medical image classifier using Multi-plane
and Multi-slice Transformer (M3T) network to classify Alzheimer's disease (AD) in 3D MRI …

Lung cancer has one of the highest mortalities of all cancers. According to the National Lung
Screening Trial, patients who underwent low-dose computed tomography (CT) scanning …

Lung cancer is one of the leading causes of cancer-related death worldwide. Early
diagnosis can effectively reduce the mortality, and computer-aided diagnosis (CAD) as an …