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Specialized Non-local Blocks for Recognizing Tumors on Computed Tomography Snapshots of Human Lungs
P. 659–664.
Aleksei Samarin, Aleksei Toropov, Dzestelova A., Nazarenko A., Kotenko E., Elena Mikhailova, Alexander Savelev, Motyko A.
This research endeavor is dedicated to the integration of specialized attentional mechanisms within the intricate web of deep neural network architectures aimed at discerning indications of lung carcinoma from monochromatic snapshots derived from computerized axial tomography. Within this exploration, we propose a myriad of adaptations to the traditional non-local blocks, infusing them with bespoke attentional nuances to resonate with the idiosyncrasies of medical imaging data. These bespoke adaptations ushered in discernible ameliorations in the performance metrics of the fundamental deep neural network model. Our solution facilitated a reduction in the model parameter count without compromising classification efficiency significantly. Additionally, it enabled a streamlined approach to feature extraction, contributing to enhanced interpretability and efficiency in the recognition process. These advancements were meticulously validated across test subsets meticulously curated from the Open Joint Monochrome Lungs Computer Tomography dataset, the Lung Image Database Consortium and Image Database Resource Initiative dataset, the Iraq-Oncology Teaching Hospital / National Center for Cancer Diseases dataset, Radiology Moscow and The Cancer Imaging Archive and from several others.
In book
Issue 1. , FRUCT Oy, 2024.
Polarization of opinions in the group: a modeling algorithm considering the dynamics of social bonds
Chebotarev V., Andreyuk D., Elizarova Anastasiya et al., Procedia Computer Science 2022 Vol. 213 No. C P. 596–601
The dynamics of opinion in a group are of interest for a number of practical purposes. In particular, consensus helps and polarization of opinions hinders cohesive teamwork. Existing approaches for modeling opinion dynamics mostly do not take into account the dynamism of social relations in a group. This paper proposes an algorithm and a program ...
Added: October 2, 2026
Aleksei Samarin, Alexander Savelev, Aleksei Toropov et al., Pattern Recognition and Image Analysis 2024 Vol. 34 No. 3 P. 855–862
Tasks related to the automation of medical data processing are becoming more urgent. Particular attention is paid to systems for monitoring and analyzing human physiological parameters. Such systems often use specialized sensors to capture biomedical images, such as infrared cameras. This article describes our study of the problem of segmenting the eye pupil and iris ...
Added: September 21, 2026
Aleksei Samarin, Alexander Savelev, Aleksei Toropov et al., Journal of Imaging 2025 Vol. 11 No. 10 Article 359
Timely identification and accurate delineation of ultra-early ischemic stroke lesions in non-contrast computed tomography (CT) scans of the human brain are of paramount importance for prompt medical intervention and improved patient outcomes. In this study, we propose a deep learning-driven methodology specifically designed for segmenting ultra-early ischemic regions, with a particular emphasis on both the ...
Added: September 21, 2026
Aleksei Samarin, Nazarenko A., Aleksei Toropov et al., , in: Proceedings of the 36th Conference of Open Innovations Association FRUCT, Helsinki, Finland, 30 October - 1 November 2024Vol. 36.: FRUCT Oy, 2024. P. 711–720.
Image enhancement is crucial in digital image processing to improve visual quality across various applications. Recent advancements in deep learning and computer vision have significantly advanced automatic color correction. While heavyweight solutions excel in quality, they demand substantial computational resources, whereas emerging lightweight models promise efficient operation on mobile devices. This study introduces a lightweight ...
Added: September 19, 2026
Wind Turbines Surface Damage Automatic Detection Using YOLOv8 with Specialized Backbone Modification
Aleksei Samarin, Mamaeva A., Aleksei Toropov et al., , in: Proceedings of the 36th Conference of Open Innovations Association FRUCT, Helsinki, Finland, 30 October - 1 November 2024Vol. 36.: FRUCT Oy, 2024. P. 702–710.
This work is devoted to incorporating specialized self-attention blocks into deep neural network-based models for detecting and quantifying damage across various components of wind turbines using images captured by unmanned aerial vehicle cameras. In our study, we introduce YOLOv8 backbone modification using a specialized self-attention mechanism, tailored to the specific characteristics of the input data. ...
Added: September 19, 2026
Aleksei Samarin, Aleksei Toropov, Kotenko E. et al., , in: Proceedings of the 37th Conference of Open Innovations Association FRUCT, Helsinki, Finland, 14-16 May 2025, Issue 1 (Full Papers)Vol. 37.: Helsinki: FRUCT Oy, 2025. P. 271–277.
This study focuses on improving iris and pupil segmentation in infrared images, a crucial task for gaze-tracking systems. We propose a lightweight image preprocessing approach, which ensures controlled data transformation, effectively eliminating parasitic reflections, noise, and contrast inconsistencies without introducing unwanted artifacts. Our approach employs analytically defined transformations, incorporating local and global filtering techniques to ...
Added: September 19, 2026
Shchuka I., Miftakhov S., Patrushev V. et al., , in: 2020 International Conference Engineering and Telecommunication (En&T).: IEEE, 2020. P. 1–5.
The paper presents a method for meal recognition, ingredient extraction and recipe suggestion in the Russian language. The proposed algorithm consists of several consecutive stages. On the first stage the model extracts a list of ingredients from a photo of the dish, based on which recipes on the second stage are selected. Two ingredient extraction ...
Added: June 14, 2026
Alexander Demidovskij, Artyom Tugaryov, Igor Salnikov et al., , in: PRICAI 2025: Trends in Artificial Intelligence: 22nd Pacific Rim International Conference on Artificial Intelligence, PRICAI 2025, Wellington, New Zealand, November 17–21, 2025, Proceedings, Part IIIVol. 16453.: Springer, 2026. P. 603–612.
The backpropagation method is the predominant method for pre-training and fine-tuning of Large Language models. At the same time, it is considerably demanding in terms of memory and hardware. Therefore, it makes fine-tuning and pre-training very expensive, harmful for the environment due to the large carbon footprint, and raises the blocks for the development of ...
Added: April 21, 2026
Springer, 2026.
This proceedings contain the papers presented at the 22nd Pacific Rim International Conference on Artificial Intelligence (PRICAI), held on November 17–21, 2025 in Wellington, New Zealand. PRICAI 2025 was co-hosted with the 40th International Conference on Image and Vision Computing New Zealand (IVCNZ 2025) and the annual conference of the New Zealand Artificial Intelligence Researchers ...
Added: April 21, 2026
Bernadotte A, Elfimov N., Menshikov I., Scientific data 2025 Vol. 13 No. 41
Accurate segmentation of brain vessels in magnetic resonance angiography (MRA) is essential for surgical procedures. Neural networks are powerful tools for medical image segmentation, but their development requires well-annotated datasets. However, publicly available MRA datasets with detailed vessel annotations are scarce. We present a dataset of 100 manually annotated brain MRA images from the IXI ...
Added: February 25, 2026
Antipkina I., Иванущенко А. В., Калабина И. А. et al., Мир психологии. Научно-методический журнал 2025 № 4(123) С. 295–316
Low-quality test items pose significant risks of biased and inaccurate assessment in higher education. In this study, multi-disciplinary test banks were examined, first, using classical test theory and then using a Large Language Model (Grok). Our findings reveal a number of problems in university test items due to methodological shortcomings rather than content inaccuracies. Based ...
Added: January 22, 2026
Dogonasheva O., Giraud A., Zakharov D. et al., Neural Networks 2025 Vol. 195 Article 108194
In this review, we examine computational models that explore the role of neural oscillations in speech perception, spanning from early auditory processing to higher cognitive stages. We focus on models that use rhythmic brain activities, such as gamma, theta, and delta oscillations, to encode phonemes, segment speech into syllables and words, and integrate linguistic elements ...
Added: October 16, 2025
Yasnitsky L., Голдобин М. А., Прикладная информатика 2025 Т. 20 № 3(117) С. 85–100
Currently, artificial intelligence methods are widely used in the practice of serial production enterprises. They are used to detect defects, classify and eliminate them, identify the causes of defects, predict the quality and properties of the resulting product, select optimal parameters of the production process, and identify and study its patterns. However, outside the field ...
Added: July 10, 2025
Podchufarov A., Galkina A. N., Ванина С. С. et al., Экономика и управление: проблемы, решения 2025 Т. 5 № 4 С. 61–74
Under modern conditions, the introduction of artificial intelligence technologies is becoming a significant factor in the development of high-tech industries. The article presents the results of a study of the prospects for the use of intelligent analytical systems in nuclear energy. The experience of foreign countries is analyzed and the features of successful projects using ...
Added: June 5, 2025
Tsybina Y., Kastalskiy I., Krivonosov M. et al., Neural Computing and Applications 2023 Vol. 34 No. 11 P. 9147 –9160
Modeling the neuronal processes underlying short-term working memory remains the focus of many theoretical studies in neuroscience. In this paper, we propose a mathematical model of a spiking neural network (SNN) which simulates the way a fragment of information is maintained as a robust activity pattern for several seconds and the way it completely disappears ...
Added: April 9, 2025
Tao M., Hao F., Wei L. et al., IEEE Transactions on Computational Social Systems 2024 Vol. 11 No. 6 P. 7373–7385
Attributed social networks are pervasive in real life and play a crucial role in shaping various aspects of society. These networks not only capture the connections between individuals but also encompass the associated attributes and characteristics. Analyzing and understanding these attributes provide insights into social behaviors, information diffusion patterns, and the formation of influential communities. ...
Added: February 25, 2025
Sadrtdinov I., Kodryan M., Pokonechny E. et al., , in: 38th Conference on Neural Information Processing Systems (NeurIPS 2024).: [б.и.], 2024. P. 58445–58479.
Added: February 19, 2025
Aleksandr Belov, Zakharov F., Litvinenko E. et al., , in: International IoT, Electronics and Mechatronics Conference, Volume 2. Proceedings of IEMTRONICS 2024. LNEE, volume 1228Vol. 1228.: Springer Publishing Company, 2025. P. 275–287.
Added: January 26, 2025
Litvinenko N., IEEE Access 2024
Added: December 9, 2024
Demidovskij A., Трутнев А. И., Тугарев А. М. et al., , in: Frontiers in Artificial Intelligence and Applications: 27th European Conference on Artificial Intelligence, 19–24 October 2024, Santiago de Compostela, SpainVol. 392.: IOS Press Ebooks, 2024. P. 3980–3986.
As modern neural network training and fine-tuning requires a lot of computational resources, there is a huge demand for novel, specialized algorithms for efficient and cost-effective training procedures. Aggressive Loss-based Elimination of Samples (ALOE) is an innovative method that operates with training samples based on losses obtained from a currently trained model or a pre-trained ...
Added: November 5, 2024
IOS Press Ebooks, 2024.
The field of AI has grown enormously since 1974, when a summer conference on Artificial Intelligence and Simulation of Behaviour was held in Brighton, UK. This milestone in the history of AI has since come to be thought of as the 1st European Conference on Artificial Intelligence (ECAI).
This book presents the proceedings of ECAI-2024, the ...
Added: November 5, 2024
Erlygin L., Zholobov V., Baklanova V. et al., , in: 2023 IEEE International Conference on Data Mining Workshops (ICDMW) 1–4 December 2023, Shanghai, China.: Shanghai: IEEE Computer Society, 2023. P. 1247–1258.
Machine learning models play a vital role in time series forecasting. These models, however, often overlook an important element: point uncertainty estimates. Incorporating these estimates is crucial for effective risk management, informed model selection, and decision-making.To address this issue, our research introduces a method for uncertainty estimation. We employ a surrogate Gaussian process regression model. ...
Added: March 20, 2024
Leonid N. Yasnitsky, Yasnitsky V., Aleksander O. Alekseev, Complexity 2021 Vol. 2021 Article 5392170
In the modern scientific literature, there are many reports about the successful application of neural network technologies for solving complex applied problems, in particular, for modeling the urban real estate market. There are neural network models that can perform mass assessment of real estate objects taking into account their construction and operational characteristics. However, these ...
Added: February 10, 2024