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

arXiv: https://arxiv.org/abs/2601.12512 · HTML: https://mlsanity.com/paper/2601.12512/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 18 Jan 2026.

## Builds on (most cited first, 13 of 13)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [Adversarial Feature Learning](https://mlsanity.com/paper/1605.09782/) (2016)
- [DualGAN: Unsupervised Dual Learning for Image-to-Image Translation](https://mlsanity.com/paper/1704.02510/) (2017)
- [Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis](https://mlsanity.com/paper/2007.07222/) (2020)
- [A Direct Approach to Robust Deep Learning Using Adversarial Networks](https://mlsanity.com/paper/1905.09591/) (2019)
- [MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint](https://mlsanity.com/paper/2007.11180/) (2020)
- [SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis](https://mlsanity.com/paper/1908.04346/) (2019)
- [Inter-Scanner Harmonization of High Angular Resolution DW-MRI using Null Space Deep Learning](https://mlsanity.com/paper/1810.04260/) (2018)
- [A Domain Adaptation Model for Carotid Ultrasound: Image Harmonization, Noise Reduction, and Impact on Cardiovascular Risk Markers](https://mlsanity.com/paper/2407.05163/) (2024)
- [Domain Adaptation of Carotid Ultrasound Images using Generative Adversarial Network](https://mlsanity.com/paper/2601.01460/) (2026)

## Abstract (arXiv)

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.