Work

Notice of Removal March 3, 2026: Domain Adaptation and Generalization of Functional Medical Data: A Systematic Survey of Brain Data

Year: 2024

Type: article

Abstract: In spite of 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 (DG) and domain adaptations (DA) under distribution shifts is an essential step in the analysis of medical data. As the first systematic review of DG and DA on functional brain signals, the paper discusses and categorizes various methods, tasks, and datasets in this field. Moreover, it discusses relevant directions for future research.

Source: ACM Computing Surveys

DOI: 10.1145/3654664

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

Institution: Sharif University of Technology

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, Iran National Science Foundation

Award: G3423493746