Efficient Combination of CNN and Transformer for Dual-Teacher Uncertainty-guided Semi-supervised Medical Image Segmentation.
Journal:
Computer methods and programs in biomedicine
Published Date:
Sep 2, 2022
Abstract
BACKGROUND AND OBJECTIVE: Deep learning-based methods for fast target segmentation of magnetic resonance imaging (MRI) have become increasingly popular in recent years. Generally, the success of deep learning methods in medical image segmentation tasks relies on a large amount of labeled data. The time-consuming and labor-intensive problem of data annotation is a major challenge in medical image segmentation tasks. The aim of this work is to enhance the segmentation of MR images using a semi-supervised learning-based method using a small amount of labeled data and a large amount of unlabeled data.