  The imaging community has increasingly adopted machine learning (ML) methods
to provide individualized imaging signatures related to disease diagnosis,
prognosis, and response to treatment. Clinical neuroscience and cancer imaging
have been two areas in which ML has offered particular promise. However, many
neurologic and neuropsychiatric diseases, as well as cancer, are often
heterogeneous in terms of their clinical manifestations, neuroanatomical
patterns or genetic underpinnings. Therefore, in such cases, seeking a single
disease signature might be ineffectual in delivering individualized precision
diagnostics. The current chapter focuses on ML methods, especially
semi-supervised clustering, that seek disease subtypes using imaging data. Work
from Alzheimer Disease and its prodromal stages, psychosis, depression, autism,
and brain cancer are discussed. Our goal is to provide the readers with a broad
overview in terms of methodology and clinical applications.
