ArticleInternational journal of computer assisted radiology and surgery2023
COVID-19 detection based on self-supervised transfer learning using chest X-ray images.
Article in International journal of computer assisted radiology and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 27 citations in OpenAlex.
- Privacy-Aware Continual Self-Supervised Learning on Multi-Window Chest Computed Tomography for Domain-Shift Robustness.Bioengineering (Basel, Switzerland) · 2025Article
- Content-based X-ray image retrieval using fusion of local neighboring patterns and deep features for lung disease detection.Radiological physics and technology · 2025Article
- Removing Adversarial Noise in X-ray Images via Total Variation Minimization and Patch-Based Regularization for Robust Deep Learning-based Diagnosis.Journal of imaging informatics in medicine · 2024Article
- Identification of veterinary and medically important blood parasites using contrastive loss-based self-supervised learning.Veterinary world · 2024Article
- Self-supervised learning framework application for medical image analysis: a review and summary.Biomedical engineering online · 2024Review
- Evaluation of Effectiveness of Self-Supervised Learning in Chest X-Ray Imaging to Reduce Annotated Images.Journal of imaging informatics in medicine · 2024Article
- Robust Medical Diagnosis: A Novel Two-Phase Deep Learning Framework for Adversarial Proof Disease Detection in Radiology Images.Journal of imaging informatics in medicine · 2024Article
- Boosting automatic COVID-19 detection performance with self-supervised learning and batch knowledge ensembling.Computers in biology and medicine · 2023Article
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Authors and funding
4 authors at 2 institutions in 1 country.
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Abstract
purposeConsidering several patients screened due to COVID-19 pandemic, computer-aided detection has strong potential in assisting clinical workflow efficiency and reducing the incidence of infections among radiologists and healthcare providers. Since many confirmed COVID-19 cases present radiological findings of pneumonia, radiologic examinations can be useful for fast detection. Therefore, chest radiography can be used to fast screen COVID-19 during the patient triage, thereby determining the priority of patient's care to help saturated medical facilities in a pandemic situation.
methodsIn this paper, we propose a new learning scheme called self-supervised transfer learning for detecting COVID-19 from chest X-ray (CXR) images. We compared six self-supervised learning (SSL) methods (Cross, BYOL, SimSiam, SimCLR, PIRL-jigsaw, and PIRL-rotation) with the proposed method. Additionally, we compared six pretrained DCNNs (ResNet18, ResNet50, ResNet101, CheXNet, DenseNet201, and InceptionV3) with the proposed method. We provide quantitative evaluation on the largest open COVID-19 CXR dataset and qualitative results for visual inspection.
resultsOur method achieved a harmonic mean (HM) score of 0.985, AUC of 0.999, and four-class accuracy of 0.953. We also used the visualization technique Grad-CAM++ to generate visual explanations of different classes of CXR images with the proposed method to increase the interpretability.
conclusionsOur method shows that the knowledge learned from natural images using transfer learning is beneficial for SSL of the CXR images and boosts the performance of representation learning for COVID-19 detection. Our method promises to reduce the incidence of infections among radiologists and healthcare providers.
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