Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …

Deep learning has become a popular tool for medical image analysis, but the limited
availability of training data remains a major challenge, particularly in the medical field where …

With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …

Acquiring images of the same anatomy with multiple different contrasts increases the
diversity of diagnostic information available in an MR exam. Yet, the scan time limitations …

Compressed sensing (CS) has been playing a key role in accelerating the magnetic
resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence …

Dysfunction of immune and vascular systems has been implicated in aging and Alzheimer
disease; however, their interrelatedness remains poorly understood. The complement …

Glioblastoma multiforme (GBM), a grade IV astrocytoma, is the most common and deadly
type of primary malignant brain tumor, with a patient's median survival rate ranging from 15 …

Cortical thickness (CT) and surface area (SA) are altered in many neuropsychiatric disorders
and are correlated with cognitive functioning. Little is known about how these components of …

Multi-contrast MRI acquisitions of an anatomy enrich the magnitude of information available
for diagnosis. Yet, excessive scan times associated with additional contrasts may be a …