Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-ray Images

• URL: http://arxiv.org/abs/2012.02278v2
• Date: Fri, 8 Jan 2021 07:10:15 GMT
• Title: Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-ray Images
• Authors: Jingxiong Li, Yaqi Wang, Shuai Wang, Jun Wang, Jun Liu, Qun Jin, Lingling Sun
• Abstract summary: Coronavirus disease 2019 (COVID-19) is one of the most destructive pandemic after millennium, forcing the world to tackle a health crisis. automated lung infections classification using chest X-ray (CXR) images could strengthen diagnostic capability when handling COVID-19. classifying COVID-19 from pneumonia cases using CXR image is a difficult task because of shared spatial characteristics, high feature variation and contrast diversity between cases. Massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learning models. Multiscale Attention Guided deep network with Soft Distance regularization is proposed to automatically classify COVID-19
• Score: 13.528353089963835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Coronavirus disease 2019 (COVID-19) is one of the most destructive pandemic after millennium, forcing the world to tackle a health crisis. Automated lung infections classification using chest X-ray (CXR) images could strengthen diagnostic capability when handling COVID-19. However, classifying COVID-19 from pneumonia cases using CXR image is a difficult task because of shared spatial characteristics, high feature variation and contrast diversity between cases. Moreover, massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learning models. To address these challenges, Multiscale Attention Guided deep network with Soft Distance regularization (MAG-SD) is proposed to automatically classify COVID-19 from pneumonia CXR images. In MAG-SD, MA-Net is used to produce prediction vector and attention from multiscale feature maps. To improve the robustness of trained model and relieve the shortage of training data, attention guided augmentations along with a soft distance regularization are posed, which aims at generating meaningful augmentations and reduce noise. Our multiscale attention model achieves better classification performance on our pneumonia CXR image dataset. Plentiful experiments are proposed for MAG-SD which demonstrates its unique advantage in pneumonia classification over cutting-edge models. The code is available at https://github.com/JasonLeeGHub/MAG-SD.

Related papers

• Performance of GAN-based augmentation for deep learning COVID-19 image classification [57.1795052451257]
  The biggest challenge in the application of deep learning to the medical domain is the availability of training data. Data augmentation is a typical methodology used in machine learning when confronted with a limited data set. In this work, a StyleGAN2-ADA model of Generative Adversarial Networks is trained on the limited COVID-19 chest X-ray image set.
  arXiv  Detail & Related papers  (2023-04-18T15:39:58Z)
• COVID-19 Pneumonia and Influenza Pneumonia Detection Using Convolutional Neural Networks [0.0]
  We developed a computer solution to support vision in differentiating between COVID-19 pneumonia, influenza virus pneumonia, and normal biomarkers. In its classification performance, the best performing model demonstrated a validation accuracy of 93% and an F1 score of 0.95.
  arXiv  Detail & Related papers  (2021-12-14T01:59:25Z)
• CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Images [2.2098092675263423]
  We propose a novel explainable deep learning framework (CXRNet) for accurate COVID-19 pneumonia detection. The proposed framework is based on a new multitask architecture, allowing for both disease classification and visual explanation. The experimental results demonstrate that the proposed method can achieve a satisfactory level of accuracy.
  arXiv  Detail & Related papers  (2021-10-20T22:50:35Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• RCoNet: Deformable Mutual Information Maximization and High-order Uncertainty-aware Learning for Robust COVID-19 Detection [12.790651338952005]
  The novel 2019 Coronavirus (COVID-19) infection has spread world widely and is currently a major healthcare challenge around the world. Due to faster imaging time and considerably lower cost than CT, detecting COVID-19 in chest X-ray (CXR) images is preferred for efficient diagnosis, assessment and treatment. We propose a novel deep network named em RCoNet$k_s$ for robust COVID-19 detection which employs em Deformable Mutual Information Maximization (DeIM), em Mixed High-order Moment Feature (MHMF) and em Multi-
  arXiv  Detail & Related papers  (2021-02-22T15:13:42Z)
• Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning [57.00601760750389]
  We present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge severity of COVID-19 lung infections that can be used for escalation or de-escalation of care.
  arXiv  Detail & Related papers  (2020-05-24T23:13:16Z)
• Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning [48.05232274463484]
  Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of affected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images.
  arXiv  Detail & Related papers  (2020-05-06T15:19:15Z)
• A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening [11.250464234368478]
  We introduce a comprehensive screening platform for the COVID-19 (a.k.a., SARS-CoV-2) pneumonia. The proposed AI-based system works on chest x-ray (CXR) images to predict whether a patient is infected with the COVID-19 disease.
  arXiv  Detail & Related papers  (2020-04-24T15:44:51Z)
• Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks [4.294650528226683]
  COVID-19 is a respiratory syndrome that resembles pneumonia. Scientists, researchers and healthcare experts are contributing for faster and automated diagnosis of COVID-19 by identifying pulmonary infections. This article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches.
  arXiv  Detail & Related papers  (2020-04-23T10:24:34Z)
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images [152.34988415258988]
  Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. To address these challenges, a novel COVID-19 Deep Lung Infection Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices.
  arXiv  Detail & Related papers  (2020-04-22T07:30:56Z)
• Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images [46.844349956057776]
  coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy. There is still lack of studies on effectively quantifying the lung infection caused by COVID-19. We propose a novel deep learning algorithm for automated segmentation of multiple COVID-19 infection regions.
  arXiv  Detail & Related papers  (2020-04-12T16:24:59Z)

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.