# Hierarchical SegNet with Channel and Context Attention for Accurate Lung Segmentation in Chest X-ray Images

arXiv: https://arxiv.org/abs/2405.12318 · HTML: https://mlsanity.com/paper/2405.12318/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 20 May 2024.

## Builds on (most cited first, 12 of 12)

- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [Deep Hierarchical Semantic Segmentation](https://mlsanity.com/paper/2203.14335/) (2022)
- [Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization](https://mlsanity.com/paper/2007.14895/) (2020)
- [Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation](https://mlsanity.com/paper/2003.10304/) (2020)
- [Information-Theoretic Testing and Debugging of Fairness Defects in Deep Neural Networks](https://mlsanity.com/paper/2304.04199/) (2023)
- [A Multimodal Intermediate Fusion Network with Manifold Learning for Stress Detection](https://mlsanity.com/paper/2403.08077/) (2024)
- [Concrete Surface Crack Detection with Convolutional-based Deep Learning Models](https://mlsanity.com/paper/2401.07124/) (2024)
- [The Correlations of Scene Complexity, Workload, Presence, and Cybersickness in a Task-Based VR Game](https://mlsanity.com/paper/2403.19019/) (2024)
- [Twin Transformer using Gated Dynamic Learnable Attention mechanism for Fault Detection and Diagnosis in the Tennessee Eastman Process](https://mlsanity.com/paper/2403.10842/) (2024)
- [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)
- [Hierarchical semantic segmentation using modular convolutional neural networks](https://mlsanity.com/paper/1710.05126/) (2017)

## Built on by (most cited first, 2 of 2)

- [Brain Tumor Classification using Vision Transformer with Selective Cross-Attention Mechanism and Feature Calibration](https://mlsanity.com/paper/2406.17670/) (2024)
- [Enhanced Fault Detection and Cause Identification Using Integrated Attention Mechanism](https://mlsanity.com/paper/2408.00033/) (2024)

## Abstract (arXiv)

Lung segmentation in chest X-ray images is a critical task in medical image analysis, enabling accurate diagnosis and treatment of various lung diseases. In this paper, we propose a novel approach for lung segmentation by integrating Hierarchical SegNet with a proposed multi-modal attention mechanism. The channel attention mechanism highlights specific feature maps or channels crucial for lung region segmentation, while the context attention mechanism adaptively weighs the importance of different spatial regions. By combining both mechanisms, the proposed mechanism enables the model to better capture complex patterns and relationships between various features, leading to improved segmentation accuracy and better feature representation. Furthermore, an attention gating mechanism is employed to integrate attention information with encoder features, allowing the model to adaptively weigh the importance of different attention features and ignore irrelevant ones. Experimental results demonstrate that our proposed approach achieves state-of-the-art performance in lung segmentation tasks, outperforming existing methods. The proposed approach has the potential to improve the accuracy and efficiency of lung disease diagnosis and treatment, and can be extended to other medical image analysis tasks.