ABSTRACT
Computed tomography is a powerful tool for medical examination, which plays a particularly important role in the investigation of acute diseases, such as COVID-19. A growing concern in relation to CT scans is the radiation to which the patients are exposed, and a lot of research is dedicated to methods and approaches to how to reduce the radiation dose in X-ray CT studies. In this paper, we propose a novel scanning protocol based on real-time monitored reconstruction for a helical chest CT using a pre-trained neural network model for COVID-19 detection as an expert. In a simulated study, for the first time, we proposed using per-slice stopping rules based on the COVID-19 detection neural network output to reduce the frequency of projection acquisition for portions of the scanning process. The proposed method allows reducing the total number of X-ray projections necessary for COVID-19 detection, and thus reducing the radiation dose, without a significant decrease in the prediction accuracy. The proposed protocol was evaluated on 163 patients from the COVID-CTset dataset, providing a mean dose reduction of 15.1% while the mean decrease in prediction accuracy amounted to only 1.9% achieving a Pareto improvement over a fixed protocol.
ABSTRACT
Various government and commercial services, including, but not limited to, e-government, fintech, banking, and sharing economy services, widely use smartphones to simplify service access and user authorization. Many organizations involved in these areas use identity document analysis systems in order to improve user personal-data-input processes. The tasks of such systems are not only ID document data recognition and extraction but also fraud prevention by detecting document forgery or by checking whether the document is genuine. Modern systems of this kind are often expected to operate in unconstrained environments. A significant amount of research has been published on the topic of mobile ID document analysis, but the main difficulty for such research is the lack of public datasets due to the fact that the subject is protected by security requirements. In this paper, we present the DLC-2021 dataset, which consists of 1424 video clips captured in a wide range of real-world conditions, focused on tasks relating to ID document forensics. The novelty of the dataset is that it contains shots from video with color laminated mock ID documents, color unlaminated copies, grayscale unlaminated copies, and screen recaptures of the documents. The proposed dataset complies with the GDPR because it contains images of synthetic IDs with generated owner photos and artificial personal information. For the presented dataset, benchmark baselines are provided for tasks such as screen recapture detection and glare detection. The data presented are openly available in Zenodo.
ABSTRACT
The variability of smooth pursuit eye movements was studied in a group of healthy subjects for horizontal apparent motion by a method that does not require direct measurements of eye movements. It was found that the individual smooth pursuit efficiencies for binocular perception in group of healthy elderly subjects (mean age 61 years) as well as in the group of healthy young adults were distinctly differentiated. Furthermore, we have not detected any age-related decrease in the fraction of subjects showing high smooth pursuit efficiencies. This fact demonstrates that the human oculomotor system is relatively resistant to the effects of aging. At the same time, an appreciable increase of percentage of persons with directional asymmetry of smooth pursuit has been found among elderly adults. A higher smooth pursuit efficiency was noticed reliably more often in the direction from left to right rather than in the opposite direction. Subject eye movements were recorded with i-View XTM Hi-Speed 1250 eye tracking system (SMI Inc.). These records confirmed that the smooth pursuit accuracy of older adults is less than that of young persons, at least in some directions of tracking.
