Disentangled representation learning has been proposed as an approach to learning
general representations even in the absence of, or with limited, supervision. A good general …

Generative models such as generative adversarial networks and autoencoders have gained
a great deal of attention in the medical field due to their excellent data generation capability …

Many studies aim to predict the degree of deformation on affected brain regions as
Alzheimer's disease (AD) progresses. However, those studies have been often limited since …

Identifying Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its
innate traits of irreversibility with subtle and gradual progression. These characteristics make …

How will my face look when I get older? Or, for a more challenging question: How will my
brain look when I get older? To answer this question one must devise (and learn from data) …

Many machine learning tasks in neuroimaging aim at modeling complex relationships
between a brain's morphology as seen in structural MR images and clinical scores and …

Accurate and realistic simulation of high-dimensional medical images has become an
important research area relevant to many AI-enabled healthcare applications. However …

The problem of building disease progression models with longitudinal data has long been
addressed with parametric mixed-effect models. They provide interpretable models at the …

Objective Recently, generative adversarial networks began to be actively studied in the field
of medical imaging. These models are used for augmenting the variation of images to …

Abstract Diabetic Macular Edema (DME) represents a significant visual impairment among
individuals with diabetes, leading to a dramatic reduction in visual acuity and potentially …