Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review.

ABSTRACT: (1) Background: Transfer learning refers to machine learning techniques that focus on acquiring knowledge from related tasks to improve generalization in the tasks of interest. In magnetic resonance imaging (MRI), transfer learning is important for developing strategies that address the variation in MR images from different imaging protocols or scanners. Additionally, transfer learning is beneficial for reutilizing machine learning models that were trained to solve different (but related) tasks to the task of interest. The aim of this review is to identify research directions, gaps in knowledge, applications, and widely used strategies among the transfer learning approaches applied in MR brain imaging; (2) Methods: We performed a systematic literature search for articles that applied trans

SUBMITTER: Valverde JM 

PROVIDER: S-EPMC8321322 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature