# Generator Versus Segmentor: Pseudo-healthy Synthesis

arXiv: https://arxiv.org/abs/2009.05722 · HTML: https://mlsanity.com/paper/2009.05722/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2020.

## Built on by (most cited first, 12 of 12)

- [U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation](https://mlsanity.com/paper/2406.02918/) (2024)
- [Endora: Video Generation Models as Endoscopy Simulators](https://mlsanity.com/paper/2403.11050/) (2024)
- [BIMCV-R: A Landmark Dataset for 3D CT Text-Image Retrieval](https://mlsanity.com/paper/2403.15992/) (2024)
- [LGS: A Light-weight 4D Gaussian Splatting for Efficient Surgical Scene Reconstruction](https://mlsanity.com/paper/2406.16073/) (2024)
- [EndoSparse: Real-Time Sparse View Synthesis of Endoscopic Scenes using Gaussian Splatting](https://mlsanity.com/paper/2407.01029/) (2024)
- [DiffRect: Latent Diffusion Label Rectification for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2407.09918/) (2024)
- [GaussianStego: A Generalizable Stenography Pipeline for Generative 3D Gaussians Splatting](https://mlsanity.com/paper/2407.01301/) (2024)
- [Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline](https://mlsanity.com/paper/2506.05175/) (2025)
- [GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation](https://mlsanity.com/paper/2407.05540/) (2024)
- [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)
- [Defense against Unauthorized Distillation in Image Restoration via Feature Space Perturbation](https://mlsanity.com/paper/2510.08925/) (2025)

## Abstract (arXiv)

This paper investigates the problem of pseudo-healthy synthesis that is defined as synthesizing a subject-specific pathology-free image from a pathological one. Recent approaches based on Generative Adversarial Network (GAN) have been developed for this task. However, these methods will inevitably fall into the trade-off between preserving the subject-specific identity and generating healthy-like appearances. To overcome this challenge, we propose a novel adversarial training regime, Generator versus Segmentor (GVS), to alleviate this trade-off by a divide-and-conquer strategy. We further consider the deteriorating generalization performance of the segmentor throughout the training and develop a pixel-wise weighted loss by muting the well-transformed pixels to promote it. Moreover, we propose a new metric to measure how healthy the synthetic images look. The qualitative and quantitative experiments on the public dataset BraTS demonstrate that the proposed method outperforms the existing methods. Besides, we also certify the effectiveness of our method on datasets LiTS. Our implementation and pre-trained networks are publicly available at https://github.com/Au3C2/Generator-Versus-Segmentor.