# An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection

arXiv: https://arxiv.org/abs/1810.10850 · HTML: https://mlsanity.com/paper/1810.10850/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2018.

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

- [Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review](https://mlsanity.com/paper/2307.13125/) (2023)
- [Constrained unsupervised anomaly segmentation](https://mlsanity.com/paper/2203.01671/) (2022)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review](https://mlsanity.com/paper/2110.12076/) (2021)
- [Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET](https://mlsanity.com/paper/2401.16363/) (2024)
- [GAN-based disentanglement learning for chest X-ray rib suppression](https://mlsanity.com/paper/2110.09134/) (2021)
- [Cross-Modality Neuroimage Synthesis: A Survey](https://mlsanity.com/paper/2202.06997/) (2022)
- [Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection](https://mlsanity.com/paper/2304.07147/) (2023)
- [Outlier-based Autism Detection using Longitudinal Structural MRI](https://mlsanity.com/paper/2202.09988/) (2022)
- [Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images](https://mlsanity.com/paper/2208.02135/) (2022)
- [Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks](https://mlsanity.com/paper/2108.02233/) (2021)
- [Looking at the whole picture: constrained unsupervised anomaly segmentation](https://mlsanity.com/paper/2109.00482/) (2021)
- [A Unified Framework for Generative Data Augmentation: A Comprehensive Survey](https://mlsanity.com/paper/2310.00277/) (2023)
- [An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training](https://mlsanity.com/paper/2206.03359/) (2022)
- [Where is the disease? Semi-supervised pseudo-normality synthesis from an abnormal image](https://mlsanity.com/paper/2106.15345/) (2021)
- [Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis](https://mlsanity.com/paper/2203.15347/) (2022)
- [Unsupervised Adaptation of Polyp Segmentation Models via Coarse-to-Fine Self-Supervision](https://mlsanity.com/paper/2308.06665/) (2023)
- [VALD-MD: Visual Attribution via Latent Diffusion for Medical Diagnostics](https://mlsanity.com/paper/2401.01414/) (2024)
- [Joint Triplet Autoencoder for Histopathological Colon Cancer Nuclei Retrieval](https://mlsanity.com/paper/2105.10262/) (2021)
- [Progressive GANomaly: Anomaly detection with progressively growing GANs](https://mlsanity.com/paper/2206.03876/) (2022)
- [Counterfactual Explanation and Instance-Generation using Cycle-Consistent Generative Adversarial Networks](https://mlsanity.com/paper/2301.08939/) (2023)
- [AutoML Systems For Medical Imaging](https://mlsanity.com/paper/2306.04750/) (2023)
- [Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation](https://mlsanity.com/paper/2101.06468/) (2021)
- [Unsupervised Anomaly Detection in MR Images using Multi-Contrast Information](https://mlsanity.com/paper/2105.00463/) (2021)
- [Generative Models for Reproducible Coronary Calcium Scoring](https://mlsanity.com/paper/2205.11967/) (2022)
- [Image class translation: visual inspection of class-specific hypotheticals and classification based on translation distance](https://mlsanity.com/paper/2408.08973/) (2024)
- [End-to-end Triple-domain PET Enhancement: A Hybrid Denoising-and-reconstruction Framework for Reconstructing Standard-dose PET Images from Low-dose PET Sinograms](https://mlsanity.com/paper/2412.03617/) (2024)
- [Weakly Supervised Pixel-Level Annotation with Visual Interpretability](https://mlsanity.com/paper/2502.17824/) (2025)
- [TagGAN: A Generative Model for Data Tagging](https://mlsanity.com/paper/2502.17836/) (2025)
- [Plasticine: A Traceable Diffusion Model for Medical Image Translation](https://mlsanity.com/paper/2512.18455/) (2025)

## Abstract (arXiv)

The identification of lesion within medical image data is necessary for diagnosis, treatment and prognosis. Segmentation and classification approaches are mainly based on supervised learning with well-paired image-level or voxel-level labels. However, labeling the lesion in medical images is laborious requiring highly specialized knowledge. We propose a medical image synthesis model named abnormal-to-normal translation generative adversarial network (ANT-GAN) to generate a normal-looking medical image based on its abnormal-looking counterpart without the need for paired training data. Unlike typical GANs, whose aim is to generate realistic samples with variations, our more restrictive model aims at producing a normal-looking image corresponding to one containing lesions, and thus requires a special design. Being able to provide a "normal" counterpart to a medical image can provide useful side information for medical imaging tasks like lesion segmentation or classification validated by our experiments. In the other aspect, the ANT-GAN model is also capable of producing highly realistic lesion-containing image corresponding to the healthy one, which shows the potential in data augmentation verified in our experiments.