Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and
clinicians with a powerful tool to examine functional connectivity across large-scale brain …

Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled
source domain to an unlabeled and unseen target domain, which is usually trained on data …

Congenital heart disease (CHD) is the most common birth defect and the leading cause of
neonate death in China. Clinical diagnosis can be based on the selected 2D key-frames …

Neuroimage processing tasks like segmentation, reconstruction, and registration are central
to the study of neuroscience. Robust deep learning strategies and architectures used to …

AI Safety is a major concern in many deep learning applications such as autonomous
driving. Given a trained deep learning model, an important natural problem is how to reliably …

Recent advances in unsupervised domain adaptation (UDA) show that transferable
prototypical learning presents a powerful means for class conditional alignment, which …

Brain diseases threaten hundreds of thousands of people over the world. Medical imaging
techniques such as MRI and CT are employed for various brain disease studies. As artificial …

Self-training based unsupervised domain adaptation (UDA) has shown great potential to
address the problem of domain shift, when applying a trained deep learning model in a …

Multimodal MRI provides complementary and clinically relevant information to probe tissue
condition and to characterize various diseases. However, it is often difficult to acquire …

Background Non-sedated MRI is gaining traction in clinical settings for visualization of the
velopharynx in children with velopharyngeal insufficiency. However, the behavioral …