bebi 21(2): e4

Research Article

Robust Heatmap Template Generation for COVID-19 Biomarker Detection

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  • @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},
        keywords={Neural Networks, Biomarkers, Covid-19, Heatmaps},
  • Mirtha Lucas
    Miguel Lerma
    Jacob Furst
    Daniela Raicu
    Year: 2021
    Robust Heatmap Template Generation for COVID-19 Biomarker Detection
    DOI: 10.4108/eai.24-2-2021.168729
Mirtha Lucas1,*, Miguel Lerma2, Jacob Furst1, Daniela Raicu1
  • 1: College of Computing and Digital Media, DePaul University, Chicago, United States
  • 2: Department of Mathematics, Northwestern University, Evanston, United States
*Contact email:


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.