2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDCOVID-IP-1.10
Paper Title PAIRFLOW: ENHANCING PORTABLE CHEST X-RAY BY FLOW-BASED DEFORMATION FOR COVID-19 DIAGNOSING
Authors Ngan Le, University of Arkansas, United States; James Sorensen, UAMS Medical College, United States; Toan Duc Bui, VinAI Research, Viet Nam; Arabinda Choudhary, UAMS Medical College, United States; Khoa Luu, University of Arkansas, United States; Hien Nguyen, University of Houston, United States
SessionCOVID-IP-1: COVID Related Image Processing 1
LocationArea C
Session Time:Monday, 20 September, 15:30 - 17:00
Presentation Time:Monday, 20 September, 15:30 - 17:00
Presentation Poster
Topic COVID-Related Image Processing: COVID-related image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract This work aims to assist physicians improve their speed and diagnostic accuracy when interpreting portable CXR (p_CXR), which are in especially high demand in the setting of the ongoing COVID-19 pandemic. In this paper, we introduce new deep learning frameworks, named PairFlow, to align and enhance the quality of p_CXR to be more consistent, and to more closely match higher quality conventional CXR (c_CXR). The contributions of this work are four folds. Firstly, a new database collection of subject-pair CXR is introduced. Secondly, a new deep learning-based alignment approach is presented to align subject-pairs dataset to obtain pixel-pairs dataset. Thirdly, a new PairFlow approach, an end-to-end invertible transfer deep learning method, to enhance the degraded quality of p_CXR. Finally, the performance of the proposed system is evaluated at both image quality and topological properties.