We present a zero-shot method using a fully self-supervised-learning procedure that does not require explicit manual or annotated labels for chest X-ray image interpretation to create a model with high performance for the multi-label classification of chest X-ray images. The method, which we ...
Smart chest X-ray worklist prioritization using artificial intelligence: a clinical workflow simulation Eur Radiol, 31 (6) (2020), pp. 3837-3845 Google Scholar 19 A. Majkowska, S. Mittal, D.F. Steiner, et al. Chest radiograph interpretation with deep learning models: assessment with radiologis...
(N = 1525) was taken from GitHub collected by Cohen et al. [50], Radiopaedia and TCIA. The pneumonia (N = 3863) and chest x-ray healthy (N = 1525) were collected from the Kaggle repository and National Institutes of Health (NIH) dataset [39].Fig. 1. shows the workflow of our ...
Gaillard F, Normal chest x-ray. Case study, Radiopaedia.org. https://radiopaedia.org/cases/8304.Accessed on 27–09- 2021 Global Situation. World Health Organization (WHO) Coronavirus (COVID-19) Dashboard. https://covid19.who.int/. Accessed on 28-09-2021 Guan WJ, Ni ZY, Hu Y, Liang...
and privacy. One potential solution lies in combining the chest X-ray imaging mode with federated deep learning, ensuring that no single data source can bias the model adversely. This study presents a pre-processing pipeline designed to debias chest X-ray images, thereby enhancing internal classifi...
For COVID-19 positive X-rays, we used the Cohen dataset. This gathers X-ray and CT images from Radiopaedia (https://radiopaedia.org/, accessed on 9 January 2021), SIRM (https://www.sirm.org/category/senza-categoria/COVID-19/, accessed on 9 January 2021), and other research articles....