  Machine learning is playing an increasing important role in medical image
analysis, spawning new advances in neuroimaging clinical applications. However,
previous work and reviews were mainly focused on the electrophysiological
signals like EEG or SEEG; the potential of neuroimaging in epilepsy research
has been largely overlooked despite of its wide use in clinical practices. In
this review, we highlight the interactions between neuroimaging and machine
learning in the context of the epilepsy diagnosis and prognosis. We firstly
outline typical neuroimaging modalities used in epilepsy clinics, \textit{e.g}
MRI, DTI, fMRI and PET. We then introduce two approaches to apply machine
learning methods to neuroimaging data: the two-step compositional approach
which combines feature engineering and machine learning classifier, and the
end-to-end approach which is usually toward deep learning. Later a detailed
review on the machine learning tasks on epileptic images is presented, such as
segmentation, localization and lateralization tasks, as well as the tasks
directly related to the diagnosis and prognosis. In the end, we discuss current
achievements, challenges, potential future directions in the field, with the
hope to pave a way to computer-aided diagnosis and prognosis of epilepsy.
