Domain Adaptation and Generalization of Functional Medical Data: A Systematic Survey of Brain Data

Gita Sarafraz, Armin Behnamnia, Mehran Hosseinzadeh, Ali Balapour, Amin Meghrazi, Hamid R. Rabiee

Abstract:

Despite the excellent capabilities of machine learning algorithms, their performance deteriorates when the
distribution of test data differs from the distribution of training data. In medical data research, this problem is
exacerbated by its connection to human health, expensive equipment, and meticulous setups. Consequently,
achieving domain generalizations and domain adaptations under distribution shifts is an essential step in the
analysis of medical data. As the first systematic review of domain generalization and domain adaptation on
functional brain signals, the article discusses and categorizes various methods, tasks, and datasets in this field.
Moreover, it discusses relevant directions for future research.