# Lung Segmentation from Chest X-rays using Variational Data Imputation

arXiv: https://arxiv.org/abs/2005.10052 · HTML: https://mlsanity.com/paper/2005.10052/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2020.

## Built on by (most cited first, 15 of 15)

- [Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder](https://mlsanity.com/paper/2105.00026/) (2021)
- [Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment](https://mlsanity.com/paper/2104.05261/) (2021)
- [Optimising Chest X-Rays for Image Analysis by Identifying and Removing Confounding Factors](https://mlsanity.com/paper/2208.10320/) (2022)
- [CXR Segmentation by AdaIN-based Domain Adaptation and Knowledge Distillation](https://mlsanity.com/paper/2104.05892/) (2021)
- [CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Images](https://mlsanity.com/paper/2110.10813/) (2021)
- [Self-Supervised Curricular Deep Learning for Chest X-Ray Image Classification](https://mlsanity.com/paper/2301.10687/) (2023)
- [Prompt-Aware Adaptive Elastic Weight Consolidation for Continual Learning in Medical Vision-Language Models](https://mlsanity.com/paper/2511.20732/) (2025)
- [Reducing Labelled Data Requirement for Pneumonia Segmentation using Image Augmentations](https://mlsanity.com/paper/2102.12764/) (2021)
- [CXR-Net: An Artificial Intelligence Pipeline for Quick Covid-19 Screening of Chest X-Rays](https://mlsanity.com/paper/2103.00087/) (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)
- [Confidence-Guided Data Augmentation for Improved Semi-Supervised Training](https://mlsanity.com/paper/2209.08174/) (2022)
- [Underestimation of lung regions on chest X-ray segmentation masks assessed by comparison with total lung volume evaluated on computed tomography](https://mlsanity.com/paper/2402.11510/) (2024)
- [A comparative analysis of deep learning models for lung segmentation on X-ray images](https://mlsanity.com/paper/2404.06455/) (2024)
- [Mitigating Dimensionality in 2D Rectangle Packing Problem under Reinforcement Learning Schema](https://mlsanity.com/paper/2409.09677/) (2024)
- [Cartesian Genetic Programming Approach for Designing Convolutional Neural Networks](https://mlsanity.com/paper/2410.00129/) (2024)

## Abstract (arXiv)

Pulmonary opacification is the inflammation in the lungs caused by many respiratory ailments, including the novel corona virus disease 2019 (COVID-19). Chest X-rays (CXRs) with such opacifications render regions of lungs imperceptible, making it difficult to perform automated image analysis on them. In this work, we focus on segmenting lungs from such abnormal CXRs as part of a pipeline aimed at automated risk scoring of COVID-19 from CXRs. We treat the high opacity regions as missing data and present a modified CNN-based image segmentation network that utilizes a deep generative model for data imputation. We train this model on normal CXRs with extensive data augmentation and demonstrate the usefulness of this model to extend to cases with extreme abnormalities.