# XLSor: A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generation

arXiv: https://arxiv.org/abs/1904.09229 · HTML: https://mlsanity.com/paper/1904.09229/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2019.

## Built on by (most cited first, 28 of 28)

- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization](https://mlsanity.com/paper/2104.05833/) (2021)
- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2107.05274/) (2021)
- [Transformers in Medical Image Analysis: A Review](https://mlsanity.com/paper/2202.12165/) (2022)
- [Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives](https://mlsanity.com/paper/2206.01136/) (2022)
- [Transformers in Healthcare: A Survey](https://mlsanity.com/paper/2307.00067/) (2023)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [De-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation](https://mlsanity.com/paper/2407.14153/) (2024)
- [Weakly Supervised Few-Shot Segmentation Via Meta-Learning](https://mlsanity.com/paper/2109.01693/) (2021)
- [Attention Mechanisms in Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.17937/) (2023)
- [Vector Quantisation for Robust Segmentation](https://mlsanity.com/paper/2207.01919/) (2022)
- [Learning to Segment Medical Images from Few-Shot Sparse Labels](https://mlsanity.com/paper/2108.05476/) (2021)
- [DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion models](https://mlsanity.com/paper/2304.13416/) (2023)
- [Unsupervised-learning-based method for chest MRI-CT transformation using structure constrained unsupervised generative attention networks](https://mlsanity.com/paper/2106.08557/) (2021)
- [Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks](https://mlsanity.com/paper/2108.02233/) (2021)
- [Improving Classification Model Performance on Chest X-Rays through Lung Segmentation](https://mlsanity.com/paper/2202.10971/) (2022)
- [Robust semi-supervised segmentation with timestep ensembling diffusion models](https://mlsanity.com/paper/2311.07421/) (2023)
- [Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks](https://mlsanity.com/paper/2308.14066/) (2023)
- [CXR Segmentation by AdaIN-based Domain Adaptation and Knowledge Distillation](https://mlsanity.com/paper/2104.05892/) (2021)
- [Employing similarity to highlight differences: On the impact of anatomical assumptions in chest X-ray registration methods](https://mlsanity.com/paper/2301.09338/) (2023)
- [Probabilistic Domain Adaptation for Biomedical Image Segmentation](https://mlsanity.com/paper/2303.11790/) (2023)
- [Hybrid diffusion models: combining supervised and generative pretraining for label-efficient fine-tuning of segmentation models](https://mlsanity.com/paper/2408.03433/) (2024)
- [Transesophageal Echocardiography Generation using Anatomical Models](https://mlsanity.com/paper/2410.06781/) (2024)
- [Analysing the effectiveness of a generative model for semi-supervised medical image segmentation](https://mlsanity.com/paper/2211.01886/) (2022)
- [Nested-block self-attention for robust radiotherapy planning segmentation](https://mlsanity.com/paper/2102.13541/) (2021)
- [CXR-Net: An Artificial Intelligence Pipeline for Quick Covid-19 Screening of Chest X-Rays](https://mlsanity.com/paper/2103.00087/) (2021)
- [Generative Residual Attention Network for Disease Detection](https://mlsanity.com/paper/2110.12984/) (2021)

## Abstract (arXiv)

This paper proposes a novel framework for lung segmentation in chest X-rays. It consists of two key contributions, a criss-cross attention based segmentation network and radiorealistic chest X-ray image synthesis (i.e. a synthesized radiograph that appears anatomically realistic) for data augmentation. The criss-cross attention modules capture rich global contextual information in both horizontal and vertical directions for all the pixels thus facilitating accurate lung segmentation. To reduce the manual annotation burden and to train a robust lung segmentor that can be adapted to pathological lungs with hazy lung boundaries, an image-to-image translation module is employed to synthesize radiorealistic abnormal CXRs from the source of normal ones for data augmentation. The lung masks of synthetic abnormal CXRs are propagated from the segmentation results of their normal counterparts, and then serve as pseudo masks for robust segmentor training. In addition, we annotate 100 CXRs with lung masks on a more challenging NIH Chest X-ray dataset containing both posterioranterior and anteroposterior views for evaluation. Extensive experiments validate the robustness and effectiveness of the proposed framework. The code and data can be found from https://github.com/rsummers11/CADLab/tree/master/Lung_Segmentation_XLSor .