# Adversarial regression training for visualizing the progression of chronic obstructive pulmonary disease with chest x-rays

arXiv: https://arxiv.org/abs/1908.10468 · HTML: https://mlsanity.com/paper/1908.10468/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2019.

## Built on by (most cited first, 6 of 6)

- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [Guidelines and Evaluation of Clinical Explainable AI in Medical Image Analysis](https://mlsanity.com/paper/2202.10553/) (2022)
- [Weakly Supervised Thoracic Disease Localization via Disease Masks](https://mlsanity.com/paper/2101.09915/) (2021)
- [Flow-Mixup: Classifying Multi-labeled Medical Images with Corrupted Labels](https://mlsanity.com/paper/2102.08148/) (2021)
- [TiBiX: Leveraging Temporal Information for Bidirectional X-ray and Report Generation](https://mlsanity.com/paper/2403.13343/) (2024)
- [A Theoretical Study of The Effects of Adversarial Attacks on Sparse Regression](https://mlsanity.com/paper/2212.11209/) (2022)

## Abstract (arXiv)

Knowledge of what spatial elements of medical images deep learning methods use as evidence is important for model interpretability, trustiness, and validation. There is a lack of such techniques for models in regression tasks. We propose a method, called visualization for regression with a generative adversarial network (VR-GAN), for formulating adversarial training specifically for datasets containing regression target values characterizing disease severity. We use a conditional generative adversarial network where the generator attempts to learn to shift the output of a regressor through creating disease effect maps that are added to the original images. Meanwhile, the regressor is trained to predict the original regression value for the modified images. A model trained with this technique learns to provide visualization for how the image would appear at different stages of the disease. We analyze our method in a dataset of chest x-rays associated with pulmonary function tests, used for diagnosing chronic obstructive pulmonary disease (COPD). For validation, we compute the difference of two registered x-rays of the same patient at different time points and correlate it to the generated disease effect map. The proposed method outperforms a technique based on classification and provides realistic-looking images, making modifications to images following what radiologists usually observe for this disease. Implementation code is available at https://github.com/ricbl/vrgan.