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 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.