# Domain Adaptation of Carotid Ultrasound Images using Generative Adversarial Network

arXiv: https://arxiv.org/abs/2601.01460 · HTML: https://mlsanity.com/paper/2601.01460/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 4 Jan 2026.

## Builds on (most cited first, 8 of 8)

- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [Resnet in Resnet: Generalizing Residual Architectures](https://mlsanity.com/paper/1603.08029/) (2016)
- [A scoping review of transfer learning research on medical image analysis using ImageNet](https://mlsanity.com/paper/2004.13175/) (2020)
- [MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint](https://mlsanity.com/paper/2007.11180/) (2020)
- [A Domain Adaptation Model for Carotid Ultrasound: Image Harmonization, Noise Reduction, and Impact on Cardiovascular Risk Markers](https://mlsanity.com/paper/2407.05163/) (2024)
- [Preserving Semantic Consistency in Unsupervised Domain Adaptation Using Generative Adversarial Networks](https://mlsanity.com/paper/2104.13725/) (2021)
- [Image translation of Ultrasound to Pseudo Anatomical Display by CycleGAN](https://mlsanity.com/paper/2202.08053/) (2022)

## Built on by (most cited first, 1 of 1)

- [Fine-Tuning Cycle-GAN for Domain Adaptation of MRI Images](https://mlsanity.com/paper/2601.12512/) (2026)

## Abstract (arXiv)

Deep learning has been extensively used in medical imaging applications, assuming that the test and training datasets belong to the same probability distribution. However, a common challenge arises when working with medical images generated by different systems or even the same system with different parameter settings. Such images contain diverse textures and reverberation noise that violate the aforementioned assumption. Consequently, models trained on data from one device or setting often struggle to perform effectively with data from other devices or settings. In addition, retraining models for each specific device or setting is labor-intensive and costly. To address these issues in ultrasound images, we propose a novel Generative Adversarial Network (GAN)-based model. We formulated the domain adaptation tasks as an image-to-image translation task, in which we modified the texture patterns and removed reverberation noise in the test data images from the source domain to align with those in the target domain images while keeping the image content unchanged. We applied the proposed method to two datasets containing carotid ultrasound images from three different domains. The experimental results demonstrate that the model successfully translated the texture pattern of images and removed reverberation noise from the ultrasound images. Furthermore, we evaluated the CycleGAN approaches for a comparative study with the proposed model. The experimental findings conclusively demonstrated that the proposed model achieved domain adaptation (histogram correlation (0.960 (0.019), & 0.920 (0.043) and bhattacharya distance (0.040 (0.020), & 0.085 (0.048)), compared to no adaptation (0.916 (0.062) & 0.890 (0.077), 0.090 (0.070) & 0.121 (0.095)) for both datasets.