Reducing annotation burden in MR: A novel MR-contrast guided contrastive learning approach for image segmentation.

ABSTRACT:

Background

Contrastive learning, a successful form of representational learning, has shown promising results in pretraining deep learning (DL) models for downstream tasks. When working with limited annotation data, as in medical image segmentation tasks, learning domain-specific local representations can further improve the performance of DL models.

Purpose

In this work, we extend the contrastive learning framework to utilize domain-specific contrast information from unlabeled Magnetic Resonance (MR) images to improve the performance of downstream MR image segmentation tasks in the presence of limited labeled data.

Methods

The contrast in MR images is controlled by underlying tissue properties (e.g., T1 or T2) and image acquisition parameters. We hypothesize that lear

SUBMITTER: Umapathy L 

PROVIDER: S-EPMC10994772 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature