# Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation

arXiv: https://arxiv.org/abs/2003.10304 · HTML: https://mlsanity.com/paper/2003.10304/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2020.

## Built on by (most cited first, 17 of 17)

- [Modality specific U-Net variants for biomedical image segmentation: A survey](https://mlsanity.com/paper/2107.04537/) (2021)
- [Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment](https://mlsanity.com/paper/2104.05261/) (2021)
- [Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A review](https://mlsanity.com/paper/2110.14910/) (2021)
- [GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-ray Images](https://mlsanity.com/paper/2107.06442/) (2021)
- [A Novel Approach to Chest X-ray Lung Segmentation Using U-net and Modified Convolutional Block Attention Module](https://mlsanity.com/paper/2404.14322/) (2024)
- [Automatic Detection of B-lines in Lung Ultrasound Videos From Severe Dengue Patients](https://mlsanity.com/paper/2102.01059/) (2021)
- [COVID-19 identification from volumetric chest CT scans using a progressively resized 3D-CNN incorporating segmentation, augmentation, and class-rebalancing](https://mlsanity.com/paper/2102.06169/) (2021)
- [Semi-supervised Learning for COVID-19 Image Classification via ResNet](https://mlsanity.com/paper/2103.06140/) (2021)
- [Hierarchical SegNet with Channel and Context Attention for Accurate Lung Segmentation in Chest X-ray Images](https://mlsanity.com/paper/2405.12318/) (2024)
- [Trilateral Attention Network for Real-time Medical Image Segmentation](https://mlsanity.com/paper/2106.09201/) (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)
- [UNet#: A UNet-like Redesigning Skip Connections for Medical Image Segmentation](https://mlsanity.com/paper/2205.11759/) (2022)
- [Self-Supervised and Semi-Supervised Polyp Segmentation using Synthetic Data](https://mlsanity.com/paper/2307.12033/) (2023)
- [Chest X-ray lung and heart segmentation based on minimal training sets](https://mlsanity.com/paper/2101.08309/) (2021)
- [B-line Detection in Lung Ultrasound Videos: Cartesian vs Polar Representation](https://mlsanity.com/paper/2107.12291/) (2021)
- [BarlowTwins-CXR : Enhancing Chest X-Ray abnormality localization in heterogeneous data with cross-domain self-supervised learning](https://mlsanity.com/paper/2402.06499/) (2024)
- [Generative AI: A Pix2pix-GAN-Based Machine Learning Approach for Robust and Efficient Lung Segmentation](https://mlsanity.com/paper/2412.10826/) (2024)

## Abstract (arXiv)

Chest X-ray is the most common test among medical imaging modalities. It is applied for detection and differentiation of, among others, lung cancer, tuberculosis, and pneumonia, the last with importance due to the COVID-19 disease. Integrating computer-aided detection methods into the radiologist diagnostic pipeline, greatly reduces the doctors' workload, increasing reliability and quantitative analysis. Here we present a novel deep learning approach for lung segmentation, a basic, but arduous task in the diagnostic pipeline. Our method uses state-of-the-art fully convolutional neural networks in conjunction with an adversarial critic model. It generalized well to CXR images of unseen datasets with different patient profiles, achieving a final DSC of 97.5% on the JSRT dataset.