Due to the proliferation of biomedical imaging modalities, such as Photoacoustic
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …

Neuroimaging-based single subject prediction of brain disorders has gained increasing
attention in recent years. Using a variety of neuroimaging modalities such as structural …

We present an approach to efficiently detect the 2D pose of multiple people in an image. The
approach uses a nonparametric representation, which we refer to as Part Affinity Fields …

Autism spectrum disorder (ASD) is a neurodevelopmental incurable disorder with a long
diagnostic period encountered in the early years of life. If diagnosed early, the negative …

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and
symptoms that appear in early childhood. ASD is also associated with communication …

Over the past decade, in-vivo MRI studies have provided many invaluable insights into the
neural substrates underlying autism spectrum disorder (ASD), which is now known to be …

Conclusion The application of machine learning methods to neuroimaging has risen more
rapidly than could have been predicted 15 years ago. It has been a very exciting new …

Standard univariate analysis of neuroimaging data has revealed a host of neuroanatomical
and functional differences between healthy individuals and patients suffering a wide range …

Abstract Machine learning techniques are increasingly being used in making relevant
predictions and inferences on individual subjects neuroimaging scan data. Previous studies …

Many classical machine learning techniques have been used to explore Alzheimer's
disease (AD), evolving from image decomposition techniques such as principal component …