Varun Buch

TL;DR: In this article, the authors used federated learning to predict future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays.

TL;DR: This study investigates the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting and shows that despite substantial differences among the datasets from all sites and without centralizing data, it can successfully train AI models in federation.

TL;DR: A hybrid weak label-based deep learning method is proposed that utilize both the manually annotated pulmonary opacities from COVID-19 pneumonia and the patient-level disease-type information available from the clinical report to predict the infected regions as well as the consolidation regions with good correlation to human annotation.

TL;DR: Wang et al. as mentioned in this paper developed a novel CXR image retrieval model based on deep metric learning, which utilizes multi-similarity loss with hard-mining sampling strategy and attention mechanism to learn the optimized embedding space, and provides similar images, visualizations of disease-related attention maps and useful clinical information to assist clinical decisions.

TL;DR: In this paper, the authors compared the performance of artificial intelligence (AI) and Radiographic Assessment of Lung Edema (RALE) scores from frontal chest radiographs (CXRs) for predicting patient outcomes and the need for mechanical ventilation in COVID-19 pneumonia.