mlsanityData to 7 Oct 2026

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

arXiv 2003.10304 ↗First posted 23 Mar 2020eess.IV cs.CV cs.LGcited by 17

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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.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Mar 2020.

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Citing papers per year: 2021: 11; 2022: 1; 2023: 1; 2024: 4.

17 papers.

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  1. 2021Modality specific U-Net variants for biomedical image segmentation: A surveycited by 20
  2. 2021Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessmentcited by 6
  3. 2021Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A reviewcited by 5
  4. 2021GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-ray Imagescited by 3
  5. 2024A Novel Approach to Chest X-ray Lung Segmentation Using U-net and Modified Convolutional Block Attention Modulecited by 3
  6. 2021Automatic Detection of B-lines in Lung Ultrasound Videos From Severe Dengue Patientscited by 2
  7. 2021COVID-19 identification from volumetric chest CT scans using a progressively resized 3D-CNN incorporating segmentation, augmentation, and class-rebalancingcited by 2
  8. 2021Semi-supervised Learning for COVID-19 Image Classification via ResNetcited by 2
  9. 2024Hierarchical SegNet with Channel and Context Attention for Accurate Lung Segmentation in Chest X-ray Imagescited by 2
  10. 2021Trilateral Attention Network for Real-time Medical Image Segmentationcited by 1
  11. 2021CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Imagescited by 1
  12. 2022UNet#: A UNet-like Redesigning Skip Connections for Medical Image Segmentationcited by 1
5 more
  1. 2023Self-Supervised and Semi-Supervised Polyp Segmentation using Synthetic Datacited by 1
  2. 2021Chest X-ray lung and heart segmentation based on minimal training setscited by 0
  3. 2021B-line Detection in Lung Ultrasound Videos: Cartesian vs Polar Representationcited by 0
  4. 2024BarlowTwins-CXR : Enhancing Chest X-Ray abnormality localization in heterogeneous data with cross-domain self-supervised learningcited by 0
  5. 2024Generative AI: A Pix2pix-GAN-Based Machine Learning Approach for Robust and Efficient Lung Segmentationcited by 0