Guided Network

Guided networks represent a class of machine learning models designed to leverage information from a source (e.g., labeled data, a reference image, or prior knowledge) to improve the performance of a target task. Current research focuses on diverse applications, including medical image segmentation, image fusion, and reinforcement learning, employing architectures such as encoder-decoder networks and transformers, often incorporating techniques like attention mechanisms and Gaussian mixture models to guide feature extraction and prediction. These advancements improve accuracy and efficiency in various fields, particularly in medical imaging and computer vision, by enhancing the quality of predictions and enabling more robust and interpretable models.