@inproceedings{9305965713c143e2977d11f903e09ee0,
title = "Heatmap Template Generation for COVID-19 Biomarker Detection in Chest X-rays",
abstract = "Detecting and identifying patterns in chest X-ray images of Covid-19 patients are important tasks for understanding the disease and for making differential diagnosis. Given the relatively small number of available Covid-19 X-ray images and the need to make progress in understanding the disease, we propose a transfer learning technique applied to a pretrained VGG19 neural network to build a deep convolutional model capable of detecting four possible conditions: normal (healthy), bacteria, virus (not Covid-19), and Covid-19. The transformation of the multi-class deep learning output into binary outputs and the detection of Covid-19 image patterns using Grad-CAM technique show promising results. The discovered patterns are consistent across images from a given class of disease and constitute explanations of how the deep learning model makes classification decisions. In the long run, the identified patterns can serve as biomarkers for a given disease in chest X-ray images.",
keywords = "Artificial Intelligence, Biomarkers, Covid-19, Neural Networks",
author = "Mirtha Lucas and Miguel Lerma and Jacob Furst and Daniela Raicu",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020 ; Conference date: 26-10-2020 Through 28-10-2020",
year = "2020",
month = oct,
doi = "10.1109/BIBE50027.2020.00077",
language = "English (US)",
series = "Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "438--445",
booktitle = "Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020",
address = "United States",
}