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August 18, 2026
HSE Scholar Presents Research on Postcards in Brazil and South Korea
Timur Khusyainov, Deputy Dean of theFaculty of Humanities atHSE University–Nizhny Novgorod, took part in two international conferences—the XVI World Congress of Rural Sociology in Porto Alegre, Brazil, and the 36th Annual Conference of the Alliance of Digital Humanities Organisations (DH2026) in Daejeon, South Korea. On his way to the conferences, the researcher also visited several other places, where he presented the experience of the Pochtovoe educational project.
August 18, 2026
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Large vortices with characteristic spiral arms are often observed in the atmosphere and the ocean. Physicists from HSE University have explained how these structures form and why they retain their shape. The researchers found that velocities at points located along the same vortex arc remain correlated even over long distances. At the same time, this correlation weakens rapidly with increasing distance from the vortex centre. These differences help explain the formation of spiral arms and may improve models of atmospheric and oceanic currents. The findings have been published in Physical Review Fluids.
August 17, 2026
‘I Dream of Simple Things
Anastasia Gergenreter specialises in applied statistics and econometrics. In this interview for the Young Scientists of HSE University project, she talked about why she studies addictive substance use, two very different Fishers, and the cherry blossom season at the Main Botanical Garden in Moscow.

 

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Localization in Medical Imaging: A Unified AI Approach for Ovaries, Follicles, and Vertebral Arteries

Algorithms. 2026. Vol. 19. No. 7. Article 523.
Moshkin A., Fedorov M., Arlazarov V., Gribova V., Nazarenko A., Repin D., Klevtsova O., Romanov A.

Artificial intelligence (AI) technologies, which are being actively developed in modern medicine today, increase the speed and quality of patient care. This article mainly seeks to demonstrate the use of various options of computer analysis of clinical images to solve practical problems of increasing the efficiency of routine diagnostics using retrospective analysis, as well as show the potential for its widespread implementation (due to the scalability of the architecture) in practical healthcare, exemplified by ultrasound (US) and magnetic resonance imaging (MRI) data analysis. This is an interuniversity study, its research protocol was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the local Ethics Committee of Orel State University named after I. S. Turgenev (Protocol No. 25 dated 16 November 2022). The software was developed using Python 3.7 and open neural network models. Statistical processing included an efficiency assessment for which IBM SPSS Statistics 20.0 was used. Detection errors in the analysis of 550 US cases did not exceed 6–8% and were associated with technical difficulties due to image quality. When studying 1030 MRI studies, only 0.19% of cases failed to obtain reliable image analysis results. The differences in the average values for the dimensional characteristics of the studied vessels were 0.11–0.12 mm. The effectiveness of AI in clinical tasks is presented. The improvement in segmentation accuracy was achieved through the use of step-by-step image optimization during the AI training stage. The evolution of technologies in medicine, aimed at digitalization and personalization, is intended to improve the quality and speed of studying images in practical work.

Research target: Computer Science Clinical Medicine Health Studies
Language: English
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DOI
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Keywords: U-NetYOLOv8YOLOv8овариальный резервovarian follicular reservevertebral artery segmentationU-Net
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