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

Aims Major depression disorder (MDD) is the single greatest cause of disability and
morbidity, and affects about 10% of the population worldwide. Currently, there are no …

In this chapter, we explore Support Vector Machine (SVM)—a machine learning method that
has become exceedingly popular for neuroimaging analysis in recent years. Because of …

Abstract Machine learning is a branch of computer science that has the potential to transform
epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new …

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 …

Recent studies have provided promising evidence that neuroimaging data can predict
treatment outcomes for patients with major depressive disorder (MDD). As most of these …

Neuroimaging‐based approaches have been extensively applied to study mental illness in
recent years and have deepened our understanding of both cognitively healthy and …

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 …

Background Postpartum depression (PPD) is a common complication of cesarean section. S-
ketamine given intravenously during surgery can help prevent PPD. However, whether S …