Transformers have dominated the field of natural language processing and have recently
made an impact in the area of computer vision. In the field of medical image analysis …

Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

With the improvement of hardware computing power and the development of deep learning
algorithms, a revolution of" artificial intelligence (AI)+ medical image" is taking place …

Abstract Over the years, Machine Learning models have been successfully employed on
neuroimaging data for accurately predicting brain age. Deviations from the healthy brain …

The gap between chronological age (CA) and biological brain age, as estimated from
magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging …

The new architecture TransUNet, which combines convolutional neural networks (CNNs)
and transformers, has displayed competitive performance in medical image segmentation. In …

Multi-label classification (MLC) can attach multiple labels on single image, and has
achieved promising results on medical images. But existing MLC methods still face …

Brain age is considered as an important biomarker for detecting aging-related diseases
such as Alzheimer's Disease (AD). Magnetic resonance imaging (MRI) have been widely …

Prediction of chronological age from neuroimaging in the healthy population is an important
issue because the deviations from normal brain age may highlight abnormal trajectories …

We propose a novel deep neural network architecture to learn interpretable representation
for medical image analysis. Our architecture generates a global attention for region of …