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Adversarial Regression Training for Visualizing the Progression of Chronic Obstructive Pulmonary Disease with Chest X-Rays

  • Ricardo Bigolin LanfrediEmail author
  • Joyce D. Schroeder
  • Clement Vachet
  • Tolga Tasdizen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)

Abstract

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.

Keywords

COPD Chest x-ray Regression interpretation Visual attribution Adversarial training Disease effect VR-GAN 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Scientific Computing and Imaging InstituteUniversity of UtahSalt Lake CityUSA
  2. 2.Department of Radiology and Imaging SciencesUniversity of UtahSalt Lake CityUSA

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