Multi-label-chest-X-ray-image-classification-via-category-wise-residual-attention-learning

KO | EN

gitlite — search

#python #java #python3 #arduino #golang #machine-learning #rust #html #flask #javascript #seismology #nodejs

Multi-label-chest-X-ray-image-classification-via-category-wise-residual-attention-learning

★ 17 Open GitHub ↗

Multi-label chest X-ray image classification via category-wise residual attention learning

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Category-wise-Residual-Attention-Learning
This paper, Multi-label chest X-ray image classification via category-wise residual attention learning, appears in [PATTERN RECOGNITION LETTERS](https://www.sciencedirect.com/science/article/pii/S0167865518308559), 2018. [[Paper]](https://github.com/guanqj932/Category-wise-Residual-Attention-Learning/blob/master/CRAL.pdf)

## Overview
In this paper, we propose a category-wise residual attention learning (CRAL) framework. CRAL predicts the presence of multiple pathologies in a class-specific attentive view. The proposed framework consists of two modules: feature embedding module and attention learning module. The feature embedding module learns high-level features with a convolutional neural network (CNN) while the attention learning module focuses on exploring the assignment scheme of different categories.

![The category-wise residual attention learning framework](https://github.com/guanqj932/Category-wise-Residual-Attention-Learning/blob/master/figure/framework.png)

## Results
![Comparision results](https://github.com/guanqj932/Category-wise-Residual-Attention-Learning/blob/master/figure/table.jpg) 
[12] S. Guendel , S. Grbic , B. Georgescu , K. Zhou , L. Ritschl , A. Meier , D. Comaniciu , Learning to recognize abnormalities in chest X-rays with location-aware dense networks, arXiv preprint arXiv:1803.04565 (2018) .  
[20] Z. Li , C. Wang , M. Han , Y. Xue , W. Wei , L.-J. Li , F.-F. Li , Thoracic disease identification and localization with limited supervision, in: CVPR, 2018, pp. 8290–8299 .  
[30] Y. Shen , M. Gao , Dynamic routing on deep neural network for thoracic disease classification and sensitive area localization, in: International Workshop on Machine Learning in Medical Imaging, Springer, 2018, pp. 389–397 .  
[34] Y. Tang , X. Wang , A.P. Harrison , L. Lu , J. Xiao , R.M. Summers , Attention-guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs, in: International Workshop on Machine Learning in Medical Imaging, Springer, 2018, pp. 249–258 .  
[37] X. Wang , Y. Peng , L. Lu , Z. Lu , M. Bagheri , R.M. Summers , Chest X-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 3462–3471 .  
[45] L. Yao , J. Prosky , E. Poblenz , B. Covington , K. Lyman , Weakly supervised medical diagnosis and localization from multiple resolutions, arXiv preprint arXiv:1803.07703 (2018) .

## Citation 
If you find the paper useful for your research, please consider cite:
```bibtex
@article{guan2018multi,
  title={Multi-label Chest X-ray Image Classification via Category-wise Residual Attention Learning},
  author={Guan, Qingji and Huang, Yaping},
  journal={Pattern Recognition Letters},
  year={2018},
  doi={https://doi.org/10.1016/j.patrec.2018.10.027},
  publisher={Elsevier}
}
```

Multi-label-chest-X-ray-image-classification-via-category-wise-residual-attention-learning

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README