# Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2108.02233 · HTML: https://mlsanity.com/paper/2108.02233/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 4 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection](https://mlsanity.com/paper/1810.10850/) (2018)
- [XLSor: A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generation](https://mlsanity.com/paper/1904.09229/) (2019)

## Built on by (most cited first, 4 of 4)

- [Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest Radiographs](https://mlsanity.com/paper/2205.04282/) (2022)
- [3D unsupervised anomaly detection and localization through virtual multi-view projection and reconstruction: Clinical validation on low-dose chest computed tomography](https://mlsanity.com/paper/2206.13385/) (2022)
- [Anomaly Detection using Generative Models and Sum-Product Networks in Mammography Scans](https://mlsanity.com/paper/2210.06188/) (2022)
- [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)

## Abstract (arXiv)

Lung nodules are commonly missed in chest radiographs. We propose and evaluate P-AnoGAN, an unsupervised anomaly detection approach for lung nodules in radiographs. P-AnoGAN modifies the fast anomaly detection generative adversarial network (f-AnoGAN) by utilizing a progressive GAN and a convolutional encoder-decoder-encoder pipeline. Model training uses only unlabelled healthy lung patches extracted from the Indiana University Chest X-Ray Collection. External validation and testing are performed using healthy and unhealthy patches extracted from the ChestX-ray14 and Japanese Society for Radiological Technology datasets, respectively. Our model robustly identifies patches containing lung nodules in external validation and test data with ROC-AUC of 91.17% and 87.89%, respectively. These results show unsupervised methods may be useful in challenging tasks such as lung nodule detection in radiographs.