# Pseudo-healthy synthesis with pathology disentanglement and adversarial learning

arXiv: https://arxiv.org/abs/2005.01607 · HTML: https://mlsanity.com/paper/2005.01607/

## Key facts

- Cited by 19 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 20 Apr 2020.

## Built on by (most cited first, 19 of 19)

- [What is Healthy? Generative Counterfactual Diffusion for Lesion Localization](https://mlsanity.com/paper/2207.12268/) (2022)
- [Learning Disentangled Representations in the Imaging Domain](https://mlsanity.com/paper/2108.12043/) (2021)
- [Detecting Outliers with Poisson Image Interpolation](https://mlsanity.com/paper/2107.02622/) (2021)
- [A Structural Causal Model for MR Images of Multiple Sclerosis](https://mlsanity.com/paper/2103.03158/) (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)
- [CF-Seg: Counterfactuals meet Segmentation](https://mlsanity.com/paper/2506.16213/) (2025)
- [Review of Disentanglement Approaches for Medical Applications -- Towards Solving the Gordian Knot of Generative Models in Healthcare](https://mlsanity.com/paper/2203.11132/) (2022)
- [LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation](https://mlsanity.com/paper/2308.09026/) (2023)
- [Synthetic Data in Radiological Imaging: Current State and Future Outlook](https://mlsanity.com/paper/2407.01561/) (2024)
- [Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification](https://mlsanity.com/paper/2203.07815/) (2022)
- [Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images](https://mlsanity.com/paper/2208.02135/) (2022)
- [Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval](https://mlsanity.com/paper/2103.12328/) (2021)
- [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)
- [Causal Disentanglement for Robust Long-tail Medical Image Generation](https://mlsanity.com/paper/2504.14450/) (2025)
- [Beyond Representation Learning: A Systematic Study of Joint-Embedding Predictive Generation for 3D Brain MRI](https://mlsanity.com/paper/2608.28787/) (2026)
- [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)
- [Interpretable Diabetic Retinopathy Diagnosis based on Biomarker Activation Map](https://mlsanity.com/paper/2212.06299/) (2022)

## Abstract (arXiv)

Pseudo-healthy synthesis is the task of creating a subject-specific `healthy' image from a pathological one. Such images can be helpful in tasks such as anomaly detection and understanding changes induced by pathology and disease. In this paper, we present a model that is encouraged to disentangle the information of pathology from what seems to be healthy. We disentangle what appears to be healthy and where disease is as a segmentation map, which are then recombined by a network to reconstruct the input disease image. We train our models adversarially using either paired or unpaired settings, where we pair disease images and maps when available. We quantitatively and subjectively, with a human study, evaluate the quality of pseudo-healthy images using several criteria. We show in a series of experiments, performed on ISLES, BraTS and Cam-CAN datasets, that our method is better than several baselines and methods from the literature. We also show that due to better training processes we could recover deformations, on surrounding tissue, caused by disease. Our implementation is publicly available at https://github.com/xiat0616/pseudo-healthy-synthesis. This paper has been accepted by Medical Image Analysis: https://doi.org/10.1016/j.media.2020.101719.