Research Article
Robust Heatmap Template Generation for COVID-19 Biomarker Detection
@ARTICLE{10.4108/eai.24-2-2021.168729, author={Mirtha Lucas and Miguel Lerma and Jacob Furst and Daniela Raicu}, title={Robust Heatmap Template Generation for COVID-19 Biomarker Detection}, journal={EAI Endorsed Transactions on Bioengineering and Bioinformatics}, volume={1}, number={2}, publisher={EAI}, journal_a={BEBI}, year={2021}, month={2}, keywords={Neural Networks, Biomarkers, Covid-19, Heatmaps}, doi={10.4108/eai.24-2-2021.168729} }- Mirtha Lucas
Miguel Lerma
Jacob Furst
Daniela Raicu
Year: 2021
Robust Heatmap Template Generation for COVID-19 Biomarker Detection
BEBI
EAI
DOI: 10.4108/eai.24-2-2021.168729
Abstract
INTRODUCTION: 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.
OBJECTIVES: The purpose of this work is to develop a technique for detecting biomarkers of four possible conditions in chest X-rays, and study the patterns arising from the location of biomarkers.
METHODS: We use transfer learning applied to a pretrained VGG19 neural network to build a model capable of detecting the four conditions in chest X-rays. For biomarkers detection we use Grad-CAM. Patterns in the biomarkers are found by using classical eigenfaces approach.
RESULTS: The discovered patterns are consistent across images from a given class of disease, and they are robust with respect to changes in dataset.
CONCLUSION: The identified patterns can serve as biomarkers for a given disease in chest X-ray images, and constitute explanations of how the deep learning model makes classification decisions.
Copyright © 2021 Mirtha Lucas et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.