PairFlow: Enhancing Portable Chest X-Ray By Flow-Based Deformation For Covid-19 Diagnosing

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 Pair-Flow, to align and enhance...

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Bibliographic Details
Main Authors: Le, Ngan, Sorensen, James, Bui, Toan Duc, Choudhary, Arabinda, Luu, Khoa, Nguyen, Hien
Format: Conference Proceeding
Language:English
Subjects:
Chest Xray
COVID
COVID-19
Deep learning
Enhancement
Flow-based Deformation
Image processing
Image quality
Image-to-Image Translation
Lung
Neurons
Pandemics
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Summary: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 Pair-Flow, 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 and available to download. Secondly, a new deep learning-based alignment approach is presented to align subject-pairs dataset to obtain pixel-pairs dataset. Thirdly, a new Pair-Flow 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.
ISSN:2381-8549
DOI:10.1109/ICIP42928.2021.9506579