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Segmentation of Vertebral Arteries on the MR Images
P. 273–278.
The vertebral arteries are one of the most important sources of blood supply to the brain, therefore any pathological changes in them can be the reason behind serious diseases. Magnetic Resonance Imaging (MRI) allows diagnosticians to examine main arteries, which is exceptionally important for effective diagnosis. However, because of the small size of arteries relative to full MRI scan diagnosticians may not be able to spot significant anomalies due to subjective factors. This paper proposes the development of an intelligent system based on neural network for the segmentation of vertebral arteries in clinical MRI images. The system provides comparative analysis in form of reports, assisting diagnosticians in accurately examining relatively small regions of vertebral arteries in clinical MRI images. The proposed service has the potential to significantly improve identification of any vertebral arteries related pathologies, which result in improving diagnostic quality.
Portnoy S., Efremov A., Voloshin A., , in: 2026 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO).: Petrozavodsk: IEEE, 2026. P. 1–4.
This paper presents a deep learning-based optimization of a list-decoding algorithm for a concatenated Hamming-Reed-Solomon code transmitted over an AWGN channel with BPSK modulation. The proposed method reformulates early termination of the erasure-pattern list as a weighted regression problem, addressing class imbalance through an asymmetric Weighted Mean Squared Error (WMSE) loss function that penalizes index ...
Added: October 1, 2026
Aleksei Samarin, Nazarenko A., Alexander Savelev et al., Pattern Recognition and Image Analysis 2024 Vol. 34 No. 3 P. 844–854
Improving image quality is becoming an increasingly popular task, especially when working with mobile devices. One common approach to image enhancement is the use of convolutional neural networks. However, to achieve good results, such networks must be large enough, otherwise there is a risk of unwanted artifacts. In addition, large convolutional neural networks require significant ...
Added: September 21, 2026
Aleksei Samarin, Aleksei Toropov, Alexander Savelev et al., , in: Pattern Recognition. ICPR 2024 International Workshops and Challenges.: Cham: Springer, 2025. P. 308–320.
This research explores an innovative approach to enhancing the accuracy of detecting small microorganisms in complex microscopic environments. Our study introduces a streamlined, hybrid image pre-processing model specifically designed to address the challenges of identifying diplococci in live microscopy of dynamic samples. By integrating pre-defined filtering techniques with predictive adjustments for optimal applicability, our method ...
Added: September 21, 2026
Lebedev O. B., Шмелева А. Г., Гежа Н. С., Информатика и автоматизация (Труды СПИИРАН) 2026 Т. 25 № 3 С. 720–750
This paper describes the development of a neural network model for automated analysis of medical data in electrophysiology based on deep learning methods. The relevance of this work stems from the growing need to improve the objectivity, speed, and accuracy of processing complex spatiotemporal signals, such as ECG or EEG. Convolutional neural networks (CNNs), which ...
Added: September 10, 2026
Vyacheslav D. Neverov, Lukyanov A., Andrey V. Krasavin et al., Physical Review B: Condensed Matter and Materials Physics 2026 Vol. 113 No. 2 Article 024515
We develop a neural network approach to solve the self-consistent Bogoliubov-de Gennes equations in strongly disordered s-wave superconductors. The method accurately reproduces inhomogeneous gap distributions and generalizes to system sizes far larger than those used in training. It reduces computational scaling from O(N6 ) to O(N2), enabling quantitative analysis of percolation phenomena and the superconductor-insulator ...
Added: September 5, 2026
Khodadoust J., Kulikova S., Khodadoust F., Biomedical Signal Processing and Control 2027 Vol. 129 Article 111284
Acute ischemic stroke (AIS) analysis from two-dimensional (2D) clinical imaging is hindered by uncontrolled slice tilt and geometric inconsistencies that violate the assumptions of pose-agnostic deep learning (DL) models. This paper proposes a unified geometry-aware, frequency-domain framework for tilted slice localization and ischemic stroke segmentation that explicitly decouples pose estimation from lesion analysis. The method ...
Added: September 2, 2026
Seul: PMLR, 2026.
Added: June 4, 2026
Davydov S. G., Федоров В. В., Социологические исследования 2026 № 5 С. 141–147
Представлены результаты измерения ИИ-грамотности взрослого населения России. Исследование решает проблему отсутствия эмпирических данных о фактическом уровне владения компетенциями в сфере искусственного интеллекта среди граждан. Методика основана на самооценке владения пятью типами ИИ-инструментов по 5‑балльной шкале и последующем индексировании. Сбор информации осуществлен методом телефонного опроса (CATI) на общероссийской выборке проекта «ВЦИОМ–Спутник» (N = 1600). Выявлен уровень ...
Added: May 13, 2026
Kristan M., Matas J., Tokmakov P. et al., , in: 2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).: Honolulu: IEEE, 2025. P. 7472–7490.
The VOTS2025 is the third edition of the Visual Object Tracking Segmentation benchmark. Organised the VOT initiative, VOTS builds on 10 years of experience in organising VOT challenges. Building on the tracking setup introduced in VOTS2023, the challenge continues to integrate short-term and long-term tracking, as well as single-target and multi-target scenarios, using segmentation masks ...
Added: May 3, 2026
Moshkin A., Наука молодых - Eruditio Juvenium 2024 Т. 12 № 2 С. 229–242
INTRODUCTION: Vertebral arteries and veins are the most vulnerable part of cerebral hemodynamics and are well accessible for visualization at ultrasound examination. AIM: To determine the relationship between the course and individual anatomical features of the vertebral arteries and veins when performing ultrasound examinations. MATERIALS AND METHODS: Two hundred thirty-three volunteers, aged from 18 to ...
Added: February 24, 2026
Moshkin A., Лапутин Ф. А., Сидоров И. В., DIGITAL DIAGNOSTICS 2024 Т. 5 № S1 С. 40–42
BACKGROUND: Ovarian reserve reflects a woman's ability to successfully realize reproductive function. The assessment of ovarian reserve is an urgent task for clinical practice [1] and is important in scientific research. The use of computerized diagnostic image processing methods can accelerate and facilitate the performance of routine tasks in clinical practice. Their use in retrospective ...
Added: February 21, 2026
Kudryavtseva V. A., Narasimha M. B., Moiseeva A. V. et al., Current Medicinal Chemistry 2025 Vol. 32 Article e09298673377087
Background: Ovarian reserve reflects the functional capacity of a woman's ovaries, encompassing factors such as follicle quantity, egg quality, and fertilization potential. Assessment of ovarian reserve is essential in reproductive medicine, particularly for fertility evaluation and assisted reproductive technologies (ART). While traditional biochemical markers such as anti-Müllerian hormone (AMH) and follicle-stimulating hormone (FSH) are commonly used, ...
Added: February 18, 2026
Tang G., Ahmadi S., Jillings S. et al., Eye 2025 Vol. 39 No. 18 P. 3294–3305
INTRODUCTION: Robust and objective quantification of ophthalmic and retroorbital structures from standard neuroimaging is an accessible but still untapped resource for clinical assessment. Current approaches mostly rely on subjective manual measurements, introducing variability and limiting clinical integration. We developed and validated an automated ocular morphometrics pipeline enabling diagnostic precision through standardised orbital measurements.
METHODS: The presented pipeline consists of: ...
Added: December 28, 2025
Gallo F., Voits T., Rothman J. et al., eNeuro 2025 Vol. 12 No. 6 Article ENEURO.0128-25.2025
Models of experience-dependent neuroplasticity predict that the acquisition and extensive use of a new skill trigger a nonlinear trajectory of neurostructural modifications, where initial expansion of relevant brain areas subsequently (once the skill is acquired) gives way to volumetric renormalization. Such predictions also apply in the domain of language during learning and/or simultaneous management of ...
Added: December 12, 2025
Dzhanashia K., Aleksandr Fedosov, Oleg Evsutin, Sensors 2025 Vol. 25 No. 23 Article 7726
Using an attack-simulation module is a well-recognized approach to improving the robustness of end-to-end neural-network-based data-hiding schemes. However, most proposed attack simulators are limited in the types of attacks they cover, usually handling only a basic set of digital transformations. Real, in-demand use cases for data-hiding methods may involve modifications that cannot be modeled by ...
Added: November 28, 2025
O.A. Goryunov, Maslennikov O. V., Kiselev M. V. et al., Chaos, Solitons and Fractals 2026 Vol. 203 Article 117663
Training complex, biologically plausible Spiking Neural Networks (SNNs) with local learning rules is a significant challenge for theoretical analysis. Here we address this problem by developing a comprehensive analytical theory for the learning dynamics of CoLaNET, a recently proposed columnar SNN. In particular, we consider a simplified model that captures the core algorithmic logic of ...
Added: November 28, 2025
Pakshin P., Актуальные проблемы российского права 2025 Т. 20 № 11 С. 11–18
The paper substantiates the necessity of providing legal protection for the results of intellectual works created by artificial intelligence through the mechanism of related rights. It examines ways to reduce legal risks associated with the creation of intellectual property using artificial intelligence technologies and offers a philosophical and legal analysis of the proposed hypothesis, namely, ...
Added: November 27, 2025
Penskaja E., Имагология и компаративистика 2025 № 23 С. 380–389
The book Artificial Intelligence, Archives and Manuscripts. New Relationships between the Virtual Archive and Its Referent (2025) is presented. This collective monograph discusses both technological and legal, intellectual issues that researchers and archivists face in automated work with manuscript heritage, artificial intelligence and neural networks. ...
Added: October 30, 2025
Surkov A., Sergei Koltcov, Ignatenko V. et al., Physica A: Statistical Mechanics and its Applications 2025 Vol. 681 Article 131085
Neural networks are powerful tools capable of achieving state-of-the-art performance across a wide range of tasks; however, their effectiveness often comes at the cost of extremely large numbers of parameters, which can hinder their deployment in resource-constrained environments. To address this issue, various pruning techniques have been proposed to reduce model size and complexity while ...
Added: October 30, 2025
Cham: Springer, 2025.
This book constitutes the refereed proceedings of 34th International Workshops which were held in conjunction with the 34th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2025, held in Kaunas, Lithuania, September 9–12, 2025.
The 20 full papers and 8 abstracts included in this workshop volume were carefully reviewed and selected from 42 submissions. ...
Added: September 29, 2025
Хашутогова У. П., Berezner T., Poddiakov A., Новые психологические исследования 2025 № 3 С. 100–125
The rapid advancement of artificial intelligence technologies has drawn increasing attention from psychological researchers. While neural networks are being integrated into nearly all domains of human activity, the boundaries of their applicability remain unclear — particularly regarding the originality and practical value of the content they generate. Proponents advocate for their widespread adoption, whereas skeptics ...
Added: September 4, 2025