Comparison and ensemble of 2D and 3D approaches for COVID-19 detection in CT images.

Detecting COVID-19 in computed tomography (CT) or radiography images has been proposed as a supplement to the RT-PCR test. We compare slice-based (2D) and volume-based (3D) approaches to this problem and propose a deep learning ensemble, called IST-CovNet, combining the best 2D and 3D systems with novel preprocessing and attention modules and the use of a bidirectional Long Short-Term Memory model for combining slice-level decisions. The proposed ensemble obtains 90.80% accuracy and 0.95 AUC score overall on the newly collected IST-C dataset in detecting COVID-19 among normal controls and other types of lung pathologies; and 93.69% accuracy and 0.99 AUC score on the publicly available MosMedData dataset that consists of COVID-19 scans and normal controls only. The system also obtains state-of-art results (90.16% accuracy and 0.94 AUC) on the COVID-CT-MD dataset which is only used for testing. The system is deployed at Istanbul University Cerrahpasa School of Medicine where it is used to automatically screen CT scans of patients, while waiting for RT-PCR tests or radiologist evaluation.

Prediction of Response Time and Vigilance Score in a Sustained Attention Task from Pre-trial Phase Synchrony using Deep Neural Networks.

A real-time assessment of sustained attention requires a continuous performance measure ideally obtained objectively and without disrupting the ongoing behavioral patterns. In this work, we investigate whether the phasic functional connectivity patterns from short- and long-range attention networks can predict the tonic performance in a long Sustained Attention to Response Task (SART). Pre-trial phase synchrony indices (PSIs) from individual experiment blocks are used as features for assessment of the proposed average cumulative vigilance score (CVS) and hit response time (HRT). Deep neural networks (DNNs) with the mean-squared-error (MSE) loss function outperformed the ones with mean-absolute-error (MAE) in 4-fold cross-validations. PSI features from the 16-20 Hz beta sub-band obtained the lowest RMSE of 0.043 and highest correlation of 0.806 for predicting the average CVS, and the alpha oscillation PSIs resulted in an RMSE of 51.91 ms and a correlation of 0.903 for predicting the mean HRT. The proposed system can be used for monitoring performance of users susceptible to hypo- or hyper-vigilance and the subsequent system adaptation without implemented eye trackers. To the best of our knowledge, functional connectivity features in general and phase locking values in particular have not been used for regression models of vigilance variations with neural networks.