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Found 4 papers, 1 papers with code
Using Deep Learning-based Features Extracted from CT scans to Predict Outcomes in COVID-19 Patients
For determining both the patient outcomes (ICU admission and death), a novel methodology is proposed by combining multi-modal features, extracted from Computed Tomography (CT) scans and Electronic Health Record (EHR) data.
Large-scale, Fast and Accurate Shot Boundary Detection through Spatio-temporal Convolutional Neural Networks
Since current datasets are not large enough to train an accurate SBD CNN, we present a new dataset containing more than 3. 5 million frames of sharp and gradual transitions.
Spectrum Monitoring for Radar Bands using Deep Convolutional Neural Networks
The core of our framework is a deep convolutional neural network (CNN) model that enables Measurement Capable Devices to identify the presence of radar signals in the radio spectrum, even when these signals are overlapped with other sources of interference, such as commercial LTE and WLAN.
Deep Activity Recognition Models with Triaxial Accelerometers
Despite the widespread installation of accelerometers in almost all mobile phones and wearable devices, activity recognition using accelerometers is still immature due to the poor recognition accuracy of existing recognition methods and the scarcity of labeled training data.
