COVIDx CXR-4: An Expanded Multi-Institutional Open-Source Benchmark Dataset for Chest X-ray Image-Based Computer-Aided COVID-19 Diagnostics

• URL: http://arxiv.org/abs/2311.17677v1
• Date: Wed, 29 Nov 2023 14:40:31 GMT
• Title: COVIDx CXR-4: An Expanded Multi-Institutional Open-Source Benchmark Dataset for Chest X-ray Image-Based Computer-Aided COVID-19 Diagnostics
• Authors: Yifan Wu, Hayden Gunraj, Chi-en Amy Tai, Alexander Wong
• Abstract summary: We introduce COVIDx CXR-4, an expanded multi-institutional open-source benchmark dataset for chest X-ray image-based computer-aided COVID-19 diagnostics. COVIDx CXR-4 expands significantly on the previous COVIDx CXR-3 dataset by increasing the total patient cohort size by greater than 2.66 times. We provide extensive analysis on the diversity of the patient demographic, imaging metadata, and disease distributions to highlight potential dataset biases.
• Score: 79.90346960083775
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The global ramifications of the COVID-19 pandemic remain significant, exerting persistent pressure on nations even three years after its initial outbreak. Deep learning models have shown promise in improving COVID-19 diagnostics but require diverse and larger-scale datasets to improve performance. In this paper, we introduce COVIDx CXR-4, an expanded multi-institutional open-source benchmark dataset for chest X-ray image-based computer-aided COVID-19 diagnostics. COVIDx CXR-4 expands significantly on the previous COVIDx CXR-3 dataset by increasing the total patient cohort size by greater than 2.66 times, resulting in 84,818 images from 45,342 patients across multiple institutions. We provide extensive analysis on the diversity of the patient demographic, imaging metadata, and disease distributions to highlight potential dataset biases. To the best of the authors' knowledge, COVIDx CXR-4 is the largest and most diverse open-source COVID-19 CXR dataset and is made publicly available as part of an open initiative to advance research to aid clinicians against the COVID-19 disease.

Related papers

• Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosis [2.15242029196761]
  Under the global COVID-19 crisis, accurate diagnosis of COVID-19 from Chest X-ray (CXR) images is critical. 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.
  arXiv  Detail & Related papers  (2023-04-25T16:56:12Z)
• Optimising Chest X-Rays for Image Analysis by Identifying and Removing Confounding Factors [49.005337470305584]
  During the COVID-19 pandemic, the sheer volume of imaging performed in an emergency setting for COVID-19 diagnosis has resulted in a wide variability of clinical CXR acquisitions. The variable quality of clinically-acquired CXRs within publicly available datasets could have a profound effect on algorithm performance. We propose a simple and effective step-wise approach to pre-processing a COVID-19 chest X-ray dataset to remove undesired biases.
  arXiv  Detail & Related papers  (2022-08-22T13:57:04Z)
• COVIDx CXR-3: A Large-Scale, Open-Source Benchmark Dataset of Chest X-ray Images for Computer-Aided COVID-19 Diagnostics [69.55060769611916]
  The use of chest X-ray (CXR) imaging as a complementary screening strategy to RT-PCR testing is increasing. Many visual perception models have been proposed for COVID-19 screening based on CXR imaging. We introduce COVIDx CXR-3, a large-scale benchmark dataset of CXR images for supporting COVID-19 computer vision research.
  arXiv  Detail & Related papers  (2022-06-08T04:39:44Z)
• COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT Images [82.74877848011798]
  We introduce COVIDx CT-3, a large-scale benchmark dataset for detection of COVID-19 cases from chest CT images. COVIDx CT-3 includes 431,205 CT slices from 6,068 patients across at least 17 countries. We examine the data diversity and potential biases of the COVIDx CT-3 dataset, finding significant geographic and class imbalances.
  arXiv  Detail & Related papers  (2022-06-07T06:35:48Z)
• The pitfalls of using open data to develop deep learning solutions for COVID-19 detection in chest X-rays [64.02097860085202]
  Deep learning models have been developed to identify COVID-19 from chest X-rays. Results have been exceptional when training and testing on open-source data. Data analysis and model evaluations show that the popular open-source dataset COVIDx is not representative of the real clinical problem.
  arXiv  Detail & Related papers  (2021-09-14T10:59:11Z)
• Randomly Initialized Convolutional Neural Network for the Recognition of COVID-19 using X-ray Images [0.0]
  coronavirus disease (COVID-19) has been declared a worldwide pandemic. One potential solution to detect COVID-19 is by analyzing the chest X-ray images using Deep Learning (DL) models. In this study, we propose a novel randomly CNN architecture for the recognition of COVID-19. The proposed CNN model yields encouraging results with 94% and 99% of accuracy for COVIDx and enhanced COVID-19 dataset, respectively.
  arXiv  Detail & Related papers  (2021-05-17T23:40:37Z)
• Deep Learning COVID-19 Features on CXR using Limited Training Data Sets [40.45289250518209]
  We propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. Experimental results show that our method achieves state-of-the-art performance and provides clinically interpretable saliency maps.
  arXiv  Detail & Related papers  (2020-04-13T03:44:42Z)
• COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images [93.0013343535411]
  We introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images. To the best of the authors' knowledge, COVID-Net is one of the first open source network designs for COVID-19 detection from CXR images. We also introduce COVIDx, an open access benchmark dataset that we generated comprising of 13,975 CXR images across 13,870 patient patient cases.
  arXiv  Detail & Related papers  (2020-03-22T12:26:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.