Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosis
Xiao Qi1, David J. Foran2, John L. Nosher3, Ilker Hacihaliloglu4,5
1: Department of Electrical and Computer Engineering, Rutgers Univeristy, New Brunswick, NJ, USA, 2: Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA, 3: Department of Radiology, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA, 4: Department of Radiology, The University of British Columbia, BC, Canada, 5: Department of Medicine, The University of British Columbia, BC, Canada
Publication date: 2023/04/26
https://doi.org/10.59275/j.melba.2023-7e96
Abstract
Under the global COVID-19 crisis, accurate diagnosis of COVID-19 from Chest X-ray (CXR) images is critical. To reduce intra- and inter-observer variability, during the radiological assessment, computer-aided diagnostic tools have been utilized to supplement medical decision-making and subsequent disease management. Computational methods with high accuracy and robustness are required for rapid triaging of patients and aiding radiologists in the interpretation of the collected data. In this study, we propose a novel multi-feature fusion network using parallel attention blocks to fuse the original CXR images and local-phase feature-enhanced CXR images at multi-scales. We examine our model on various COVID-19 datasets acquired from different organizations to assess the generalization ability. Our experiments demonstrate that our method achieves state-of-art performance and has improved generalization capability, which is crucial for widespread deployment.
Keywords
COVID-19 · Chest X-ray · Image Enhancement · Multi-Scale Fusion · Self-Attention
Bibtex
@article{melba:2023:008:qi,
title = "Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosis",
author = "Qi, Xiao and Foran, David J. and Nosher, John L. and Hacihaliloglu, Ilker",
journal = "Machine Learning for Biomedical Imaging",
volume = "2",
issue = "April 2023 issue",
year = "2023",
pages = "236--252",
issn = "2766-905X",
doi = "https://doi.org/10.59275/j.melba.2023-7e96",
url = "https://melba-journal.org/2023:008"
}
RIS
TY - JOUR
AU - Qi, Xiao
AU - Foran, David J.
AU - Nosher, John L.
AU - Hacihaliloglu, Ilker
PY - 2023
TI - Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosis
T2 - Machine Learning for Biomedical Imaging
VL - 2
IS - April 2023 issue
SP - 236
EP - 252
SN - 2766-905X
DO - https://doi.org/10.59275/j.melba.2023-7e96
UR - https://melba-journal.org/2023:008
ER -