Newer Is Better? Comparing Deep Learning Architectures for Cardiomegaly Detection on Chest Radiographs

Congress:

ECR 2019

Poster Number:

C-3644

Type:

Scientific Exhibit

Keywords:

Oedema, Computer Applications-General, Computer Applications-Detection, diagnosis, Digital radiography, Cardiovascular system, Artificial Intelligence

Authors:

J. W. Luo, J. J. R. Chong; Montreal, QC/CA

DOI:

10.26044/ecr2019/C-3644

DOI-Link:

https://dx.doi.org/10.26044/ecr2019/C-3644

References

Dimopoulos K, Giannakoulas G, Bendayan I, et al. Cardiothoracic ratio from postero-anterior chest radiographs: A simple, reproducible and independent marker of disease severity and outcome in adults with congenital heart disease. Int J Cardiol. 2013;166(2):453-457. doi:10.1016/J.IJCARD.2011.10.125
Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. Vol 2017-Janua. ; 2017:3462-3471. doi:10.1109/CVPR.2017.369
Rajpurkar P, Irvin J, Zhu K, et al. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. November 2017. http://arxiv.org/abs/1711.05225. Accessed January 11, 2019.