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 …

Hurricanes are among the most devastating natural disasters in the United States, causing
billions of dollars of property damage and insured losses. During extreme wind events …

In this work, we propose to resolve the issue existing in current deep learning based organ
segmentation systems that they often produce results that do not capture the overall shape …

Semi-supervised learning is a useful tool for image segmentation, mainly due to its ability in
extracting knowledge from unlabeled data to assist learning from labeled data. This paper …

There have been considerable debates over 2D and 3D representation learning on 3D
medical images. 2D approaches could benefit from large-scale 2D pretraining, whereas they …

Semantic segmentation involves extracting meaningful information from images or input
from a video or recording frames. It is the way to perform the extraction by checking pixels by …

Deep learning for three dimensional (3D) abdominal organ segmentation on high-resolution
computed tomography (CT) is a challenging topic, in part due to the limited memory provide …

Segmenting breast tumors from dynamic contrast-enhanced magnetic resonance (DCE-MR)
images is a critical step for early detection and diagnosis of breast cancer. However …

Accurate grading of gliomas is crucial for treatment strategies and prognosis. While
convolutional neural networks (CNNs) have proven effective in classifying medical images …

Automated colorectal cancer (CRC) segmentation in medical imaging is the key to achieving
automation of CRC detection, staging, and treatment response monitoring. Compared with …