Awesome Open Source
Awesome Open Source

Awesome GAN for Medical Imaging

A curated list of awesome GAN resources in medical imaging, inspired by the other awesome-* initiatives.

For a complete list of GANs in general computer vision, please visit really-awesome-gan.

To complement or correct it, please contact me at [email protected] or send a pull request.



Low Dose CT Denoising

  • [Generative Adversarial Networks for Noise Reduction in Low-Dose CT] [scholar] [TMI]
  • [Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss] [scholar] [arXiv]
  • [Sharpness-aware Low dose CT denoising using conditional generative adversarial network] [scholar] [arXiv] [JDI] [code]
  • [Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography] [scholar] [arXiv]
  • [Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising] [scholar] [arXiv]
  • [3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network] [scholar] [arXiv]
  • [CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement] [scholar] [MLMI2018]
  • [TomoGAN: Low-Dose X-Ray Tomography with Generative Adversarial Networks] [scholar] [arXiv]


  • [SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation] [scholar] [arXiv]
  • [Adversarial training and dilated convolutions for brain MRI segmentation] [scholar] [arXiv]
  • [Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks] [scholar] [arXiv]
  • [Automatic Liver Segmentation Using an Adversarial Image-to-Image Network] [scholar] [arXiv]
  • [Deep Adversarial Networks for Biomedical Image Segmentation Utilizing Unannotated Images] [scholar] [MICCAI17]
  • [SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays] [scholar] [arXiv]
  • [Adversarial Deep Structured Nets for Mass Segmentation from Mammograms] [scholar] [arXiv] [code]
  • [Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth] [scholar] [arXiv]
  • [Adversarial neural networks for basal membrane segmentation of microinvasive cervix carcinoma in histopathology images] [scholar] [ICMLC]
  • [Unsupervised domain adaptation in brain lesion segmentation with adversarial networks] [scholar] [IPMI2017]
  • [whole heart and great vessel segmentation with context aware generative adversarial network] [scholar] [BM]
  • [Generative Adversarial Neural Networks for Pigmented and Non-Pigmented Skin Lesions Detection in Clinical Images] [scholar] [CSCS2017]
  • [Generative Adversarial Networks to Segment Skin Lesions] [scholar] [ISBI2018]
  • [A conditional adversarial network for semantic segmentation of brain tumor] [scholar] [arXiv]
  • [Brain Tumor Segmentation Using an Adversarial Network] [scholar] [MICCAI Brainlesion workshop]
  • [Joint Optic Disc and Cup Segmentation using Fully Convolutional and Adversarial Networks] [scholar] [OMIA2017]
  • [Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks] [scholar] [SPIE MI]
  • [CC-GAN A Robust Transfer-Learning Framework for HEp-2 Specimen Image Segmentation] [scholar] [TA]
  • [Conditional Generative Refinement Adversarial Networks for Unbalanced Medical Image Semantic Segmentation] [scholar] [arXiv]
  • [Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning] [scholar] [arXiv]
  • [Multi-input and dataset-invariant adversarial learning (MDAL) for left and right-ventricular coverage estimation in cardiac MRI] [scholar] [MICCAI2018]
  • [Spine-GAN: Semantic segmentation of multiple spinal structures] [scholar] [MedIA]
  • [Adversarial Networks for the Detection of Aggressive Prostate Cancer] [scholar] [arXiv]


  • [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery] [scholar] [arXiv]
  • [Generative adversarial networks for brain lesion detection] [scholar] [JMI]
  • [Visual Feature Attribution using Wasserstein GANs] [scholar] [arXiv]
  • [Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders] [scholar] [arXiv]
  • [Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior] [scholar] [arXiv]
  • [Deep Adversarial Context-Aware Landmark Detection for Ultrasound Imaging] [scholar] [arXiv]

Medical Image Synthesis

  • [Medical Image Synthesis with Context-Aware Generative Adversarial Networks] [scholar] [arXiv]
  • [Medical Image Synthesis with Deep Convolutional Adversarial Networks] [scholar] [TBME] (published vision of the above preprint)
  • [Deep MR to CT Synthesis using Unpaired Data] [scholar] [arXiv]
  • [Synthesizing Filamentary Structured Images with GANs] [scholar] [arXiv] [code]
  • [Synthesizing retinal and neuronal images with generative adversarial nets] [scholar] [MedIA] (published vision of the above preprint)
  • [Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks] [scholar] (GANs) [arXiv]
  • [Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks] [scholar] [arXiv]
  • [Synthetic Medical Images from Dual Generative Adversarial Networks] [scholar] [arXiv]
  • [Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results] [scholar] [arXiv]
  • [Towards Adversarial Retinal Image Synthesis] [scholar] [arXiv]
  • [End-to-end Adversarial Retinal Image Synthesis] [scholar] [TMI] (published vision of the above preprint)
  • [Adversarial Image Synthesis for Unpaired Multi-Modal Cardiac Data] [scholar] [SASHIMI 2017]
  • [Biomedical Data Augmentation Using Generative Adversarial Neural Networks] [scholar] [ICANN 2017]
  • [Towards Virtual H&E Staining of Hyperspectral Lung Histology Images Using Conditional Generative Adversarial Networks] [scholar] [ICCV2017 workshop]
  • [How to Fool Radiologists with Generative Adversarial Networks? A Visual Turing Test for Lung Cancer Diagnosis] [scholar] [arXiv]
  • [Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training] [scholar] [arXiv]
  • [Unsupervised Histopathology Image Synthesis] [scholar] [arXiv]
  • [Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks] [scholar] [arXiv]
  • [Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network] [scholar] [arXiv]
  • [Towards cross-modal organ translation and segmentation: A cycle- and shape-consistent generative adversarial network] [scholar] [MedIA]
  • [MRI Image-to-Image Translation for Cross-Modality Image Registration and Segmentation] [scholar] [arXiv]
  • [Cross-modality image synthesis from unpaired data using CycleGAN: Effects of gradient consistency loss and training data size] [scholar] [arXiv]
  • [Cross-Modality Synthesis from CT to PET using FCN and GAN Networks for Improved Automated Lesion Detection] [scholar] [arXiv]
  • [MelanoGANs: High Resolution Skin Lesion Synthesis with GANs] [scholar] [arXiv]
  • [Domain-adversarial neural networks to address the appearance variability of histopathology images] [scholar] [arXiv]
  • [Neural Stain-Style Transfer Learning using GAN for Histopathological Images] [scholar] [arXiv]
  • [Generative Adversarial Training for MRA Image Synthesis Using Multi-Contrast MRI] [scholar] [arXiv]
  • [Histopathology Stain-Color Normalization Using Generative Neural Networks] [scholar] [MIDL2018]
  • [3D cGAN based cross-modality MR image synthesis for brain tumor segmentation] [scholar] [ISBI2018]
  • [Deep CT to MR Synthesis using Paired and Unpaired Data] [scholar] [arXiv]
  • [GAN-based synthetic brain MR image generation] [scholar] [ISBI2018]
  • [StainGAN: Stain Style Transfer for Digital Histological Images] [scholar] [arXiv]
  • [Adversarial Stain Transfer for Histopathology Image Analysis] [scholar] [TMI]
  • [Chest x-ray generation and data augmentation for cardiovascular abnormality classification] [scholar] [SPIE MI2018]
  • [blood vessel geometry synthesis using generative adversarial networks] [scholar] [MIDL2018]
  • [Synergistic Reconstruction and Synthesis via Generative Adversarial Networks for Accelerated Multi-Contrast MRI] [scholar] [arXiv]
  • [Stain normalization of histopathology images using generative adversarial networks] [scholar] [ISBI2018]
  • [MedGAN: Medical Image Translation using GANs] [scholar] [arXiv]
  • [Retinal Image Synthesis for CAD Development] [scholar] [ICIAR]
  • [High-Resolution Mammogram Synthesis using Progressive Generative Adversarial Networks] [scholar] [arXiv]
  • [High-resolution medical image synthesis using progressively grown generative adversarial networks] [scholar] [arXiv]
  • [Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training] [scholar] [MICCAI2018]
  • [Generation of structural MR images from amyloid PET: Application to MR-less quantification] [scholar] [JNM]
  • [Task Driven Generative Modeling for Unsupervised Domain Adaptation Application to X-ray Image Segmentation] [scholar] [arXiv]
  • [Exploring the potential of generative adversarial networks for synthesizing radiological images of the spine to be used in in silico trials] [scholar] [FBB]
  • [Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation] [scholar] [arXiv]
  • [Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks] [scholar] [arXiv]
  • [Learning implicit brain MRI manifolds with deep learning] [scholar] [SPIE MI2018]
  • [Optimized generation of high-resolution phantom images using cGAN: Application to quantification of Ki67 breast cancer images] [scholar] [PloS one]
  • [Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images-A Comparison of CycleGAN and UNIT] [scholar] [arXiv] [code]
  • [Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks] [scholar] [SASHIMI2018]
  • [Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks] [scholar] [arXiv]
  • [Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images] [scholar] [MICCAI2018]
  • [An Adversarial Learning Approach to Medical Image Synthesis for Lesion Removal] [scholar] [arXiv]
  • [Craniomaxillofacial Bony Structures Segmentation from MRI with Deep-Supervision Adversarial Learning] [scholar] [MICCAI2018]
  • [Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network] [scholar] [MICCAI2018]
  • [GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks] [scholar] [arXiv]
  • [Generating Highly Realistic Images of Skin Lesions with GANs] [scholar] [CARE2018]
  • [Generative Adversarial Network for Medical Images (MI-GAN)] [scholar] [JMS]
  • [Retinal Vessel Segmentation under Extreme Low Annotation: A Generative Adversarial Network Approach] [scholar] [arXiv]
  • [SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth] [scholar] [TMI]
  • [Synthesizing Missing PET from MRI with Cycle-consistent Generative Adversarial Networks for Alzheimer's Disease Diagnosis] [scholar] [MICCAI2018]
  • [Tumor-Aware, Adversarial Domain Adaptation from CT to MRI for Lung Cancer Segmentation] [scholar] [MICCAI2018]
  • [Unpaired Brain MR-to-CT Synthesis Using a Structure-Constrained CycleGAN] [scholar] [DLMIA2018]
  • [Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks] [scholar] [SASHIMI2018]
  • [ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans] [scholar] [DLMIA2018]
  • [Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy] [scholar] [PMB]
  • [ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging Synthesis with Generative Adversarial Networks] [scholar] [NeurIPS 2018 ML4H]
  • [Red blood cell image generation for data augmentation using Conditional Generative Adversarial Networks] [scholar] [CVPR19 CVMI]
  • [SUSAN: segment unannotated image structure using adversarial network] [scholar] [MRM]
  • [CT synthesis from MR images for orthopedic applications in the lower arm using a conditional generative adversarial network] [scholar] [arXiv]
  • [Learning Bone Suppression from Dual Energy Chest X-rays using Adversarial Networks] [scholar] [arXiv]
  • [Simulating Patho-realistic Ultrasound Images using Deep Generative Networks with Adversarial Learning] [scholar] [ISBI2018]
  • [Stain-transforming cycle-consistent generative adversarial networks for improved segmentation of renal histopathology] [MIDL2019]
  • [Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization] [MIDL2019]
  • [Image Synthesis with a Convolutional Capsule Generative Adversarial Network] [MIDL2019]
  • [CT-realistic data augmentation using generative adversarial network for robust lymph node segmentation] [scholar] [SPIE MI2019]
  • [Unsupervisedly Training GANs for Segmenting Digital Pathology with Automatically Generated Annotations] [MIDL2019]
  • [DavinciGAN: Unpaired Surgical Instrument Translation for Data Augmentation] [MIDL2019]
  • [XLSor: A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generation] [MIDL2019]
  • [Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss] [scholar] [arXiv]
  • [CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation] [scholar] [MICCAI2018]
  • [Mask2Lesion: Mask-Constrained Adversarial Skin Lesion Image Synthesis] [scholar] [MICCAI SASHIMI 2019] [arXiv]
  • [A modality conversion approach to MV-DRs and kV-DRRs registration using information bottlenecked conditional generative adversarial network] [scholar] [MP]
  • [Leveraging Regular Fundus Images for Training UWF Fundus Diagnosis Models via Adversarial Learning and Pseudo-Labeling] [scholar] [TMI 2021] [arXiv]
  • [Synthesis of brain tumor multicontrast MR images for improved data augmentation] [scholar] [MP] [code]


  • [Compressed Sensing MRI Reconstruction with Cyclic Loss in Generative Adversarial Networks] [scholar] [arXiv]
  • [Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Loss] [scholar] [TMI] (published version of the above preprint)
  • [Deep Generative Adversarial Networks for Compressed Sensing (GANCS) Automates MRI] [scholar] [arXiv] [code]
  • [Accelerated Magnetic Resonance Imaging by Adversarial Neural Network] [scholar] [DLMIA MICCAI 2017]
  • [Deep De-Aliasing for Fast Compressive Sensing MR] [scholar] [arXiv]
  • [3D conditional generative adversarial networks for high-quality PET image estimation at low dose] [scholar] [NI]
  • [Improving resolution of MR images with an adversarial network incorporating images with different contrast] [scholar] [MP]
  • [CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)] [scholar] [arXiv]
  • [Denoising of 3-D Magnetic Resonance Images Using a Residual Encoder-Decoder Wasserstein Generative Adversarial Network] [scholar] [arXiv]
  • [Sparse-View CT Reconstruction Using Wasserstein GANs] [scholar] [MLMIR 2018]
  • [DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction] [scholar] [TMI] [code]
  • [Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction] [scholar] [MICCAI2018]
  • [Refacing: reconstructing anonymized facial features using GANs] [scholar] [arXiv]
  • [Adversarial Inpainting of Medical Image Modalities] [scholar] [arXiv]
  • [Adversarial Sparse-View CBCT Artifact Reduction] [scholar] [MICCAI2018]
  • [Cardiac MR Motion Artefact Correction from K-space Using Deep Learning-Based Reconstruction] [scholar] [MLMIR 2018]
  • [Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear] [scholar] [MICCAI2018]
  • [Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network] [scholar] [MICCAI2018]
  • [Multi-channel Generative Adversarial Network for Parallel Magnetic Resonance Image Reconstruction in K-space] [scholar] [MICCAI2018]
  • [Retrospective correction of Rigid and Non-Rigid MR motion artifacts using GANs] [scholar] [arXiv]
  • [Brain MRI super-resolution using 3D generative adversarial networks] [scholar] [MIDL2018]
  • [Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution] [scholar] [arXiv]
  • [Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy] [scholar] [MIDL2018]
  • [How Can We Make GAN Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale Approach] [scholar] [arXiv]
  • [X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks] [scholar] [CVPR2019]


  • [Semi-supervised Assessment of Incomplete LV Coverage in Cardiac MRI Using Generative Adversarial Nets] [scholar] [SASHIMI 2017]
  • [Generalization of Deep Neural Networks for Chest Pathology Classification in X-Rays Using Generative Adversarial Networks] [scholar] [arXiv]
  • [Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial Networks] [scholar] [arXiv] [code]
  • [Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification] [scholar] [arXiv]
  • [GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification] [scholar] [arXiv](extended version of above preprint)
  • [Unsupervised and semi-supervised learning with Categorical Generative Adversarial Networks assisted by Wasserstein distance for dermoscopy image Classification] [scholar] [arXiv]
  • [Semi-supervised learning with generative adversarial networks for chest X-ray classification with ability of data domain adaptation] [scholar] [ISBI2018]
  • [Generative adversarial learning for reducing manual annotation in semantic segmentation on large scale miscroscopy images: Automated vessel segmentation in retinal fundus image as test case] [scholar] [CVPRW2017]
  • [Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images] [scholar] [NeurIPS 2018 ML4H]
  • [Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease] [scholar] [NDM]
  • [General-to-Detailed GAN for Infrequent Class Medical Images] [[scholar]](General-to-Detailed GAN for Infrequent Class Medical Images) [NeurIPS 2018 ML4H]
  • [Towards generative adversarial networks as a new paradigm for radiology education] [scholar] [NeurIPS 2018 ML4H]
  • [Abnormal Chest X-ray Identification With Generative Adversarial One-Class Classifier] [scholar] [ISBI2019]


  • [Adversarial Image Registration with Application for MR and TRUS Image Fusion] [scholar] [arXiv]
  • [Deformable medical image registration using generative adversarial networks] [scholar] [ISBI2018]
  • [Generative Adversarial Networks for MR-CT Deformable Image Registration] [scholar] [arXiv]
  • [Adversarial Deformation Regularization for Training Image Registration Neural Networks] [scholar] [MICCAI2018]
  • [Adversarial Similarity Network for Evaluating Image Alignment in Deep Learning Based Registration] [scholar] [MICCAI2018]
  • [Joint Registration And Segmentation Of Xray Images Using Generative Adversarial Networks] [scholar] [MLMI2018]


  • [Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks] [scholar] [arXiv]
  • [Generative Spatiotemporal Modeling Of Neutrophil Behavior] [scholar] [ISBI2018]
  • [Exploiting the potential of unlabeled endoscopic video data with self-supervised learning] [scholar] [IJCARS]
  • [Adversarial Attacks Against Medical Deep Learning Systems] [scholar] [arXiv]
  • [Distribution Matching Losses Can Hallucinate Features in Medical Image Translation] [scholar] [MICCAI2018]
  • [Generalizability vs. Robustness: Investigating Medical Imaging Networks Using Adversarial Examples] [scholar] [MICCAI2018]
  • [CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learning] [scholar] [arXiv]
  • [Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks] [scholar] [NeurIPS 2018 ML4H]
  • [Modelling the Progression of Alzheimer’s Disease in MRI Using Generative Adversarial Networks] [scholar] [SPIE MI2018]

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