Fatemeh Homayounieh

TL;DR: In this article, a modularized neural network for low-dose CT (LDCT) was proposed and compared with commercial iterative reconstruction methods from three leading CT vendors, and the learned workflow allows radiologists-in-the-loop to optimize the denoising depth in a task-specific fashion.

TL;DR: Deep learning algorithm in its present version is unlikely to replace radiologists due to its limited specificity for categorizing specific findings, however, it can aid in interpretation of CXR findings and their stability over follow up CxR.

TL;DR: It is shown that the deep learning approach, combined with the feedback from radiologists, produces higher quality reconstructions than or similar to that using the current commercial methods.

TL;DR: Important aspects of CT in COVID-19 infection are reviewed from the justification of its use to specific scan protocols for optimizing radiation dose and diagnostic information and non-specific and overlap with other viral infections including influenza and H1N1.

TL;DR: CovidCTNet as discussed by the authors is an open-source framework composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases.