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EXAM: An Explainable Attention-based Model for COVID-19 Automatic Diagnosis

Authors:

May D. WangAuthors Info & Claims

BCB '20: Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

Article No.: 36, Pages 1 - 6

https://doi.org/10.1145/3388440.3412455

Published: 10 November 2020 Publication History

Abstract

The ongoing coronavirus disease 2019 (COVID-19) is still rapidly spreading and has caused over 7,000,000 infection cases and 400,000 deaths around the world. To come up with a fast and reliable COVID-19 diagnosis system, people seek help from machine learning area to establish computer-aided diagnosis systems with the aid of the radiological imaging techniques, like X-ray imaging and computed tomography imaging. Although artificial intelligence based architectures have achieved great improvements in performance, most of the models are still seemed as a black box to researchers. In this paper, we propose an Explainable Attention-based Model (EXAM) for COVID-19 automatic diagnosis with convincing visual interpretation. We transform the diagnosis process with radiological images into an image classification problem differentiating COVID-19, normal and community-acquired pneumonia (CAP) cases. Combining channel-wise and spatial-wise attention mechanism, the proposed approach can effectively extract key features and suppress irrelevant information. Experiment results and visualization indicate that EXAM outperforms recent state-of-art models and demonstrate its interpretability.

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Cited By

Brima YAtemkeng M(2024)Saliency-driven explainable deep learning in medical imaging: bridging visual explainability and statistical quantitative analysisBioData Mining10.1186/s13040-024-00370-417:1Online publication date: 22-Jun-2024
https://doi.org/10.1186/s13040-024-00370-4
Chundru VSait Kilinc MLim ANnamdi MZhong YShi WWang M(2024)Quantitative Explainability Study of Deformable Convolutional Neural Networks using Chest X-rays2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM62325.2024.10822072(5908-5913)Online publication date: 3-Dec-2024
https://doi.org/10.1109/BIBM62325.2024.10822072
Muhammad DBendechache M(2024)Unveiling the Black Box: A Systematic Review of Explainable Artificial Intelligence in Medical Image AnalysisComputational and Structural Biotechnology Journal10.1016/j.csbj.2024.08.005Online publication date: Aug-2024
https://doi.org/10.1016/j.csbj.2024.08.005
Show More Cited By

Index Terms

EXAM: An Explainable Attention-based Model for COVID-19 Automatic Diagnosis
1. Applied computing
  1. Life and medical sciences
    1. Computational biology
      1. Imaging
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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cover image ACM Conferences

BCB '20: Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

September 2020

193 pages

ISBN:9781450379649

DOI:10.1145/3388440

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGBio: ACM Special Interest Group on Bioinformatics

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 November 2020

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Petit Institute Faculty Fellow
Carol Ann and David Flanagan Faculty Fellow
Amazon Research Fauclty Fellow
Microsoft Azure Cloud

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BCB '20

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SIGBio

BCB '20: 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

September 21 - 24, 2020

Virtual Event, USA

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Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

Brima YAtemkeng M(2024)Saliency-driven explainable deep learning in medical imaging: bridging visual explainability and statistical quantitative analysisBioData Mining10.1186/s13040-024-00370-417:1Online publication date: 22-Jun-2024
https://doi.org/10.1186/s13040-024-00370-4
Chundru VSait Kilinc MLim ANnamdi MZhong YShi WWang M(2024)Quantitative Explainability Study of Deformable Convolutional Neural Networks using Chest X-rays2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM62325.2024.10822072(5908-5913)Online publication date: 3-Dec-2024
https://doi.org/10.1109/BIBM62325.2024.10822072
Muhammad DBendechache M(2024)Unveiling the Black Box: A Systematic Review of Explainable Artificial Intelligence in Medical Image AnalysisComputational and Structural Biotechnology Journal10.1016/j.csbj.2024.08.005Online publication date: Aug-2024
https://doi.org/10.1016/j.csbj.2024.08.005
Ben Tamo JShi WZhu YNnamdi MIwinski HWattenbarger MWang MWang MYoon B(2023)Adolescent Idiopathic Scoliosis Patient Subphenotyping for Surgical Planning and Improved Patient OutcomesProceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics10.1145/3584371.3612957(1-10)Online publication date: 3-Sep-2023
https://dl.acm.org/doi/10.1145/3584371.3612957
Giuste FShi WZhu YNaren TIsgut MSha YTong LGupte MWang M(2023)Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic ReviewIEEE Reviews in Biomedical Engineering10.1109/RBME.2022.318595316(5-21)Online publication date: 2023
https://doi.org/10.1109/RBME.2022.3185953
Shi WMurakoso MGuo XXiong LChen MWang M(2023)Effective Surrogate Models for Docking Scores Prediction of Candidate Drug Molecules on SARS-CoV-2 Protein Targets2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM58861.2023.10385643(4235-4242)Online publication date: 5-Dec-2023
https://doi.org/10.1109/BIBM58861.2023.10385643
Chandra SBajpai M(2023)Efficient Machine Learning and Factional Calculus Based Mathematical Model for Early COVID PredictionHuman-Centric Intelligent Systems10.1007/s44230-023-00042-23:4(508-520)Online publication date: 31-Aug-2023
https://doi.org/10.1007/s44230-023-00042-2
Zhu YMahale APeters KMathew LGiuste FAnderson BWang M(2022)Using natural language processing on free-text clinical notes to identify patients with long-term COVID effectsProceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3535508.3545555(1-9)Online publication date: 7-Aug-2022
https://dl.acm.org/doi/10.1145/3535508.3545555
Lee BMartirosyan SKhan ZChiu HWang ZShi WGiuste FZhong YSun JWang M(2022)Attention-based Automated Chest CT Image Segmentation Method of COVID-19 Lung Infection2022 IEEE 22nd International Conference on Bioinformatics and Bioengineering (BIBE)10.1109/BIBE55377.2022.00042(158-163)Online publication date: Nov-2022
https://doi.org/10.1109/BIBE55377.2022.00042
Li YHsu JBari NQiu XViswanathan MShi WGiuste FZhong YSun JWang M(2022)Interpretable Evaluation of Diabetic Retinopathy Grade Regarding Eye Color Fundus Images2022 IEEE 22nd International Conference on Bioinformatics and Bioengineering (BIBE)10.1109/BIBE55377.2022.00011(11-16)Online publication date: Nov-2022
https://doi.org/10.1109/BIBE55377.2022.00011
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