Multi-scale semi-supervised clustering of brain images: Deriving disease subtypes.

ABSTRACT: Disease heterogeneity is a significant obstacle to understanding pathological processes and delivering precision diagnostics and treatment. Clustering methods have gained popularity for stratifying patients into subpopulations (i.e., subtypes) of brain diseases using imaging data. However, unsupervised clustering approaches are often confounded by anatomical and functional variations not related to a disease or pathology of interest. Semi-supervised clustering techniques have been proposed to overcome this and, therefore, capture disease-specific patterns more effectively. An additional limitation of both unsupervised and semi-supervised conventional machine learning methods is that they typically model, learn and infer from data using a basis of feature sets pre-defined at a fixed anatomi

SUBMITTER: Wen J 

PROVIDER: S-EPMC8678373 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature