# DeScarGAN: Disease-Specific Anomaly Detection with Weak Supervision

arXiv: https://arxiv.org/abs/2007.14118 · HTML: https://mlsanity.com/paper/2007.14118/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2020.

## Built on by (most cited first, 13 of 13)

- [Diffusion Models for Medical Anomaly Detection](https://mlsanity.com/paper/2203.04306/) (2022)
- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [Anomaly Detection in Medical Imaging -- A Mini Review](https://mlsanity.com/paper/2108.11986/) (2021)
- [Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images](https://mlsanity.com/paper/2110.01761/) (2021)
- [CardiacNet: Learning to Reconstruct Abnormalities for Cardiac Disease Assessment from Echocardiogram Videos](https://mlsanity.com/paper/2410.20769/) (2024)
- [Contrastive Counterfactual Visual Explanations With Overdetermination](https://mlsanity.com/paper/2106.14556/) (2021)
- [Leveraging Conditional Generative Models in a General Explanation Framework of Classifier Decisions](https://mlsanity.com/paper/2106.10947/) (2021)
- [Histogram- and Diffusion-Based Medical Out-of-Distribution Detection](https://mlsanity.com/paper/2310.08654/) (2023)
- [Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models](https://mlsanity.com/paper/2406.02653/) (2024)
- [Weighing Features of Lung and Heart Regions for Thoracic Disease Classification](https://mlsanity.com/paper/2105.12430/) (2021)
- [Enhancing Generative Networks for Chest Anomaly Localization through Automatic Registration-Based Unpaired-to-Pseudo-Paired Training Data Translation](https://mlsanity.com/paper/2207.10324/) (2022)
- [Ensemble uncertainty as a criterion for dataset expansion in distinct bone segmentation from upper-body CT images](https://mlsanity.com/paper/2208.09216/) (2022)
- [Weakly-Supervised Lung Nodule Segmentation via Training-Free Guidance of 3D Rectified Flow](https://mlsanity.com/paper/2604.08313/) (2026)

## Abstract (arXiv)

Anomaly detection and localization in medical images is a challenging task, especially when the anomaly exhibits a change of existing structures, e.g., brain atrophy or changes in the pleural space due to pleural effusions. In this work, we present a weakly supervised and detail-preserving method that is able to detect structural changes of existing anatomical structures. In contrast to standard anomaly detection methods, our method extracts information about the disease characteristics from two groups: a group of patients affected by the same disease and a healthy control group. Together with identity-preserving mechanisms, this enables our method to extract highly disease-specific characteristics for a more detailed detection of structural changes. We designed a specific synthetic data set to evaluate and compare our method against state-of-the-art anomaly detection methods. Finally, we show the performance of our method on chest X-ray images. Our method called DeScarGAN outperforms other anomaly detection methods on the synthetic data set and by visual inspection on the chest X-ray image data set.