# Unsupervised Medical Image Translation Using Cycle-MedGAN

arXiv: https://arxiv.org/abs/1903.03374 · HTML: https://mlsanity.com/paper/1903.03374/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2019.

## Built on by (most cited first, 30 of 44)

- [Image-to-Image Translation: Methods and Applications](https://mlsanity.com/paper/2101.08629/) (2021)
- [Anomaly Detection in Medical Imaging -- A Mini Review](https://mlsanity.com/paper/2108.11986/) (2021)
- [Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation](https://mlsanity.com/paper/2305.05424/) (2023)
- [Conditional Frechet Inception Distance](https://mlsanity.com/paper/2103.11521/) (2021)
- [Uncertainty-Guided Progressive GANs for Medical Image Translation](https://mlsanity.com/paper/2106.15542/) (2021)
- [CyTran: A Cycle-Consistent Transformer with Multi-Level Consistency for Non-Contrast to Contrast CT Translation](https://mlsanity.com/paper/2110.06400/) (2021)
- [MRI Reconstruction with Side Information using Diffusion Models](https://mlsanity.com/paper/2303.14795/) (2023)
- [Assessing the ability of generative adversarial networks to learn canonical medical image statistics](https://mlsanity.com/paper/2204.12007/) (2022)
- [Is Image-to-Image Translation the Panacea for Multimodal Image Registration? A Comparative Study](https://mlsanity.com/paper/2103.16262/) (2021)
- [Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection](https://mlsanity.com/paper/2304.07147/) (2023)
- [Simultaneous super-resolution and motion artifact removal in diffusion-weighted MRI using unsupervised deep learning](https://mlsanity.com/paper/2105.00240/) (2021)
- [Bridging the gap between paired and unpaired medical image translation](https://mlsanity.com/paper/2110.08407/) (2021)
- [Anatomically constrained CT image translation for heterogeneous blood vessel segmentation](https://mlsanity.com/paper/2210.01713/) (2022)
- [Multi-Texture GAN: Exploring the Multi-Scale Texture Translation for Brain MR Images](https://mlsanity.com/paper/2102.07225/) (2021)
- [Deep neural networks can stably solve high-dimensional, noisy, non-linear inverse problems](https://mlsanity.com/paper/2206.00934/) (2022)
- [ChrSNet: Chromosome Straightening using Self-attention Guided Networks](https://mlsanity.com/paper/2207.00147/) (2022)
- [DIMA: DIffusing Motion Artifacts for unsupervised correction in brain MRI images](https://mlsanity.com/paper/2504.06767/) (2025)
- [ASL to PET Translation by a Semi-supervised Residual-based Attention-guided Convolutional Neural Network](https://mlsanity.com/paper/2103.05116/) (2021)
- [Synthetic PET via Domain Translation of 3D MRI](https://mlsanity.com/paper/2206.05618/) (2022)
- [Brain MRI-to-PET Synthesis using 3D Convolutional Attention Networks](https://mlsanity.com/paper/2211.12082/) (2022)
- [ContourDiff: Unpaired Medical Image Translation with Structural Consistency](https://mlsanity.com/paper/2403.10786/) (2024)
- [Anatomical Conditioning for Contrastive Unpaired Image-to-Image Translation of Optical Coherence Tomography Images](https://mlsanity.com/paper/2404.05409/) (2024)
- [Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET](https://mlsanity.com/paper/2406.12632/) (2024)
- [CAS-GAN for Contrast-free Angiography Synthesis](https://mlsanity.com/paper/2410.08490/) (2024)
- [Multi-task Deep Learning for Cerebrovascular Disease Classification and MRI-to-PET Translation](https://mlsanity.com/paper/2202.06142/) (2022)
- [Counterfactual Explanation and Instance-Generation using Cycle-Consistent Generative Adversarial Networks](https://mlsanity.com/paper/2301.08939/) (2023)
- [PaCaNet: A Study on CycleGAN with Transfer Learning for Diversifying Fused Chinese Painting and Calligraphy](https://mlsanity.com/paper/2301.13082/) (2023)
- [Cross-Modality Translation with Generative Adversarial Networks to Unveil Alzheimer's Disease Biomarkers](https://mlsanity.com/paper/2405.05462/) (2024)
- [Efficient Semantic Diffusion Architectures for Model Training on Synthetic Echocardiograms](https://mlsanity.com/paper/2409.19371/) (2024)
- [Enhancing frozen histological section images using permanent-section-guided deep learning with nuclei attention](https://mlsanity.com/paper/2411.06583/) (2024)

## Abstract (arXiv)

Image-to-image translation is a new field in computer vision with multiple potential applications in the medical domain. However, for supervised image translation frameworks, co-registered datasets, paired in a pixel-wise sense, are required. This is often difficult to acquire in realistic medical scenarios. On the other hand, unsupervised translation frameworks often result in blurred translated images with unrealistic details. In this work, we propose a new unsupervised translation framework which is titled Cycle-MedGAN. The proposed framework utilizes new non-adversarial cycle losses which direct the framework to minimize the textural and perceptual discrepancies in the translated images. Qualitative and quantitative comparisons against other unsupervised translation approaches demonstrate the performance of the proposed framework for PET-CT translation and MR motion correction.