# DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising

arXiv: https://arxiv.org/abs/2108.10772 · HTML: https://mlsanity.com/paper/2108.10772/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 49 references that are in the map.
- First posted on arXiv 24 Aug 2021.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss](https://mlsanity.com/paper/1708.00961/) (2017)
- [A U-Net Based Discriminator for Generative Adversarial Networks](https://mlsanity.com/paper/2002.12655/) (2020)
- [Self-Supervised GANs via Auxiliary Rotation Loss](https://mlsanity.com/paper/1811.11212/) (2018)
- [Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT](https://mlsanity.com/paper/1708.08333/) (2017)
- [COCO-GAN: Generation by Parts via Conditional Coordinating](https://mlsanity.com/paper/1904.00284/) (2019)
- [Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?](https://mlsanity.com/paper/1811.03691/) (2018)
- [3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network](https://mlsanity.com/paper/1802.05656/) (2018)
- [Quadratic Autoencoder (Q-AE) for Low-dose CT Denoising](https://mlsanity.com/paper/1901.05593/) (2019)
- [Sharpness-aware Low dose CT denoising using conditional generative adversarial network](https://mlsanity.com/paper/1708.06453/) (2017)
- [PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction](https://mlsanity.com/paper/1703.09165/) (2017)
- [Shape and Margin-Aware Lung Nodule Classification in Low-dose CT Images via Soft Activation Mapping](https://mlsanity.com/paper/1810.12494/) (2018)
- [A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising](https://mlsanity.com/paper/1705.04267/) (2017)

## Built on by (most cited first, 30 of 46)

- [CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization](https://mlsanity.com/paper/2304.01814/) (2023)
- [Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation](https://mlsanity.com/paper/2405.14802/) (2024)
- [ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising](https://mlsanity.com/paper/2307.12225/) (2023)
- [Learning to Distill Global Representation for Sparse-View CT](https://mlsanity.com/paper/2308.08463/) (2023)
- [LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and Deblurring](https://mlsanity.com/paper/2302.10630/) (2023)
- [Robust Split Federated Learning for U-shaped Medical Image Networks](https://mlsanity.com/paper/2212.06378/) (2022)
- [DenoMamba: A fused state-space model for low-dose CT denoising](https://mlsanity.com/paper/2409.13094/) (2024)
- [Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstruction](https://mlsanity.com/paper/2506.22012/) (2025)
- [Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation](https://mlsanity.com/paper/2405.12223/) (2024)
- [Quad-Net: Quad-domain Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/2207.11678/) (2022)
- [GAN Inversion for Data Augmentation to Improve Colonoscopy Lesion Classification](https://mlsanity.com/paper/2205.02840/) (2022)
- [Low-dose CT Denoising with Language-engaged Dual-space Alignment](https://mlsanity.com/paper/2403.06128/) (2024)
- [Nest-DGIL: Nesterov-optimized Deep Geometric Incremental Learning for CS Image Reconstruction](https://mlsanity.com/paper/2308.03807/) (2023)
- [Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI with Simultaneous Motion Estimation and Super-Resolution](https://mlsanity.com/paper/2211.11144/) (2022)
- [LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language Models](https://mlsanity.com/paper/2507.06140/) (2025)
- [Multi-Scale Texture Loss for CT denoising with GANs](https://mlsanity.com/paper/2403.16640/) (2024)
- [WiTUnet: A U-Shaped Architecture Integrating CNN and Transformer for Improved Feature Alignment and Local Information Fusion](https://mlsanity.com/paper/2404.09533/) (2024)
- [One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns](https://mlsanity.com/paper/2404.16292/) (2024)
- [Unsupervised Training of a Dynamic Context-Aware Deep Denoising Framework for Low-Dose Fluoroscopic Imaging](https://mlsanity.com/paper/2411.00830/) (2024)
- [Anatomy-Aware Low-Dose CT Denoising via Pretrained Vision Models and Semantic-Guided Contrastive Learning](https://mlsanity.com/paper/2508.07788/) (2025)
- [FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoising](https://mlsanity.com/paper/2508.17299/) (2025)
- [Transforming Image Generation from Scene Graphs](https://mlsanity.com/paper/2207.00545/) (2022)
- [Cross-domain Denoising for Low-dose Multi-frame Spiral Computed Tomography](https://mlsanity.com/paper/2304.10839/) (2023)
- [MVMS-RCN: A Dual-Domain Unfolding CT Reconstruction with Multi-sparse-view and Multi-scale Refinement-correction](https://mlsanity.com/paper/2405.17141/) (2024)
- [Iterative CT Reconstruction via Latent Variable Optimization of Shallow Diffusion Models](https://mlsanity.com/paper/2408.03156/) (2024)
- [A novel attention mechanism for noise-adaptive and robust segmentation of microtubules in microscopy images](https://mlsanity.com/paper/2507.07800/) (2025)
- [Dual Path Learning -- learning from noise and context for medical image denoising](https://mlsanity.com/paper/2507.19035/) (2025)
- [MAN: Latent Diffusion Enhanced Multistage Anti-Noise Network for Efficient and High-Quality Low-Dose CT Image Denoising](https://mlsanity.com/paper/2509.23603/) (2025)
- [A Denoising Framework for Real-World Ultra-Low-Dose Lung CT Images Based on an Image Purification Strategy](https://mlsanity.com/paper/2510.07492/) (2025)
- [Two-stage MR Image Segmentation Method for Brain Tumors based on Attention Mechanism](https://mlsanity.com/paper/2304.08072/) (2023)

## Abstract (arXiv)

LDCT has drawn major attention in the medical imaging field due to the potential health risks of CT-associated X-ray radiation to patients. Reducing the radiation dose, however, decreases the quality of the reconstructed images, which consequently compromises the diagnostic performance. Various deep learning techniques have been introduced to improve the image quality of LDCT images through denoising. GANs-based denoising methods usually leverage an additional classification network, i.e. discriminator, to learn the most discriminate difference between the denoised and normal-dose images and, hence, regularize the denoising model accordingly; it often focuses either on the global structure or local details. To better regularize the LDCT denoising model, this paper proposes a novel method, termed DU-GAN, which leverages U-Net based discriminators in the GANs framework to learn both global and local difference between the denoised and normal-dose images in both image and gradient domains. The merit of such a U-Net based discriminator is that it can not only provide the per-pixel feedback to the denoising network through the outputs of the U-Net but also focus on the global structure in a semantic level through the middle layer of the U-Net. In addition to the adversarial training in the image domain, we also apply another U-Net based discriminator in the image gradient domain to alleviate the artifacts caused by photon starvation and enhance the edge of the denoised CT images. Furthermore, the CutMix technique enables the per-pixel outputs of the U-Net based discriminator to provide radiologists with a confidence map to visualize the uncertainty of the denoised results, facilitating the LDCT-based screening and diagnosis. Extensive experiments on the simulated and real-world datasets demonstrate superior performance over recently published methods both qualitatively and quantitatively.