Align, Attend and Locate: Chest X-Ray Diagnosis via Contrast Induced Attention Network with Limited Supervision
Abstract
Obstacles facing accurate identification and localization of diseases in chest X-ray images lie in the lack of high-quality images and annotations. In this paper, we propose a Contrast Induced Attention Network (CIA-Net), which exploits the highly structured property of chest X-ray images and localizes diseases via contrastive learning on the aligned positive and negative samples. To force the attention module to focus only on sites of abnormalities, we also introduce a learnable alignment module to adjust all the input images, which eliminates variations of scales, angles, and displacements of X-ray images generated under bad scan conditions. We show that the use of contrastive attention and alignment module allows the model to learn rich identification and localization information using only a small amount of location annotations, resulting in state-of-the-art performance in NIH chest X-ray dataset.
Cite
Text
Liu et al. "Align, Attend and Locate: Chest X-Ray Diagnosis via Contrast Induced Attention Network with Limited Supervision." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019. doi:10.1109/ICCV.2019.01073Markdown
[Liu et al. "Align, Attend and Locate: Chest X-Ray Diagnosis via Contrast Induced Attention Network with Limited Supervision." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.](https://mlanthology.org/iccv/2019/liu2019iccv-align/) doi:10.1109/ICCV.2019.01073BibTeX
@inproceedings{liu2019iccv-align,
title = {{Align, Attend and Locate: Chest X-Ray Diagnosis via Contrast Induced Attention Network with Limited Supervision}},
author = {Liu, Jingyu and Zhao, Gangming and Fei, Yu and Zhang, Ming and Wang, Yizhou and Yu, Yizhou},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
year = {2019},
doi = {10.1109/ICCV.2019.01073},
url = {https://mlanthology.org/iccv/2019/liu2019iccv-align/}
}