Lung cancer is a leading cause of death worldwide and early screening is...
The universal model emerges as a promising trend for medical image
segme...
Human readers or radiologists routinely perform full-body multi-organ
mu...
Medical image benchmarks for the segmentation of organs and tumors suffe...
Multi-modal learning focuses on training models by equally combining mul...
Assessment of myocardial viability is essential in diagnosis and treatme...
Self-supervised learning (SSL) opens up huge opportunities for better
ut...
The domain gap caused mainly by variable medical image quality renders a...
Convolutional neural networks (CNNs) have been the de facto standard for...
Automated and accurate 3D medical image segmentation plays an essential ...
It has been widely recognized that the success of deep learning in image...
Due to the intensive cost of labor and expertise in annotating 3D medica...
Accurate and automated gland segmentation on histology tissue images is ...
Coronaviruses are important human and animal pathogens. To date the nove...
Recently, Attention-Gated Convolutional Neural Networks (AGCNNs) perform...
Activation functions play a key role in providing remarkable performance...
Automated skin lesion segmentation on dermoscopy images is an essential ...
A multi-level deep ensemble (MLDE) model that can be trained in an 'end ...
Network embedding (NE) is playing a principal role in network mining, du...
The Classification of medical images and illustrations in the literature...