Jannis Born

TL;DR: A more prominent role of point-of-care ultrasound imaging to guide COVID-19 detection is advocated, and an open-access web service is provided that deploys the predictive model, allowing to perform predictions on ultrasound lung images.

TL;DR: This work proposes a frame-based convolutional neural network that correctly classifies COVID-19 US videos with a sensitivity and specificity and employs class activation maps for the spatio-temporal localization of pulmonary biomarkers, which subsequently validate for human-in-the-loop scenarios in a blindfolded study with medical experts.

TL;DR: In this article, a multimodal attention-based convolutional encoder was proposed for interpretable prediction of anticancer compound sensitivity using protein-protein interaction networks (PIPI).

TL;DR: A preliminary review of the literature on research community efforts against COVID-19 pandemic is presented to make it possible for computer vision researchers to find existing and future research directions.

TL;DR: A frame-based model is proposed that correctly distinguishes COVID-19 LUS videos from healthy and bacterial pneumonia data with a sensitivity and specificity and might aid the development of a fast, accessible screening method for pulmonary diseases.