Hirofumi Obinata

TL;DR: It is shown that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity.

TL;DR: The clinical features of people infected on board the Diamond Princess cruise ship who were diagnosed with asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or mild or severe COVID-19, on admission to the Self-Defense Forces Central Hospital (Tokyo, Japan) and at the end of observation are described.

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: In this paper, a federated semi-supervised learning framework was proposed to handle the variability in both the data and annotations for detecting Coronavirus Disease 2019 (COVID-19) in chest CT scans.

TL;DR: A novel federated semi-supervised learning technique is proposed to fully utilize all available data (with or without annotations) and avoids the need for sensitive data-sharing, which makes it favorable for institutions and nations with strict regulatory policy on data privacy.