Deep Q-learning to globally optimize a k-D parameter search for medical imaging.

ABSTRACT:

Background

Estimation of the global optima of multiple model parameters is valuable for precisely extracting parameters that characterize a physical environment. This is especially useful for imaging purposes, to form reliable, meaningful physical images with good reproducibility. However, it is challenging to avoid different local minima when the objective function is nonconvex. The problem of global searching of multiple parameters was formulated to be a k-D move in the parameter space and the parameter updating scheme was converted to be a state-action decision-making problem.

Methods

We proposed a novel Deep Q-learning of Model Parameters (DQMP) method for global optimization which updated the parameter configurations through actions that maximized the Q-value and

SUBMITTER: Zhang H 

PROVIDER: S-EPMC10423342 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature