Fine-Tuning Cycle-GAN for Domain Adaptation of MRI Images

TL;DR

This paper introduces a Cycle-GAN-based method for unsupervised domain adaptation in MRI imaging, improving model robustness across different scanners and protocols without requiring paired data.

Contribution

The study presents a novel application of Cycle-GANs with content and disparity loss for effective unsupervised domain adaptation in MRI images, preserving anatomical content.

Findings

Improved model performance across different MRI domains.

Reduced domain-related variability in medical images.

Effective bidirectional adaptation without labeled data.

Abstract

Magnetic Resonance Imaging (MRI) scans acquired from different scanners or institutions often suffer from domain shifts owing to variations in hardware, protocols, and acquisition parameters. This discrepancy degrades the performance of deep learning models trained on source domain data when applied to target domain images. In this study, we propose a Cycle-GAN-based model for unsupervised medical-image domain adaptation. Leveraging CycleGANs, our model learns bidirectional mappings between the source and target domains without paired training data, preserving the anatomical content of the images. By leveraging Cycle-GAN capabilities with content and disparity loss for adaptation tasks, we ensured image-domain adaptation while maintaining image integrity. Several experiments on MRI datasets demonstrated the efficacy of our model in bidirectional domain adaptation without labelled data. Furthermore, research offers promising avenues for improving the diagnostic accuracy of healthcare. The statistical results confirm that our approach improves model performance and reduces domain-related variability, thus contributing to more precise and consistent medical image analysis.