Zobrazeno 1 - 10
of 65
pro vyhledávání: '"Kondrateva, Ekaterina"'
Autor:
Kondrateva, Ekaterina, Druzhinina, Polina, Dalechina, Alexandra, Zolotova, Svetlana, Golanov, Andrey, Shirokikh, Boris, Belyaev, Mikhail, Kurmukov, Anvar
Magnetic resonance imaging (MRI) data is heterogeneous due to differences in device manufacturers, scanning protocols, and inter-subject variability. A conventional way to mitigate MR image heterogeneity is to apply preprocessing transformations such
Externí odkaz:
http://arxiv.org/abs/2204.05278
Autor:
Kondrateva, Ekaterina, Druzhinina, Polina, Dalechina, Alexandra, Zolotova, Svetlana, Golanov, Andrey, Shirokikh, Boris, Belyaev, Mikhail, Kurmukov, Anvar
Publikováno v:
In Biomedical Signal Processing and Control October 2024 96 Part A
Autor:
Aliev, Ruslan, Kondrateva, Ekaterina, Sharaev, Maxim, Bronov, Oleg, Marinets, Alexey, Subbotin, Sergey, Bernstein, Alexander, Burnaev, Evgeny
Focal cortical dysplasia (FCD) is one of the most common epileptogenic lesions associated with cortical development malformations. However, the accurate detection of the FCD relies on the radiologist professionalism, and in many cases, the lesion cou
Externí odkaz:
http://arxiv.org/abs/2010.10373
Autor:
Pominova, Marina, Kondrateva, Ekaterina, Sharaev, Maxim, Bernstein, Alexander, Burnaev, Evgeny
Publikováno v:
ICMV2020
ABIDE is the largest open-source autism spectrum disorder database with both fMRI data and full phenotype description. These data were extensively studied based on functional connectivity analysis as well as with deep learning on raw data, with top m
Externí odkaz:
http://arxiv.org/abs/2010.07233
Autor:
Kondrateva, Ekaterina, Pominova, Marina, Popova, Elena, Sharaev, Maxim, Bernstein, Alexander, Burnaev, Evgeny
Publikováno v:
ICMV2020
Machine learning and computer vision methods are showing good performance in medical imagery analysis. Yetonly a few applications are now in clinical use and one of the reasons for that is poor transferability of themodels to data from different sour
Externí odkaz:
http://arxiv.org/abs/2010.07222
Autor:
Kan, Maxim, Aliev, Ruslan, Rudenko, Anna, Drobyshev, Nikita, Petrashen, Nikita, Kondrateva, Ekaterina, Sharaev, Maxim, Bernstein, Alexander, Burnaev, Evgeny
Publikováno v:
AIST2020
Deep learning shows high potential for many medical image analysis tasks. Neural networks can work with full-size data without extensive preprocessing and feature generation and, thus, information loss. Recent work has shown that the morphological di
Externí odkaz:
http://arxiv.org/abs/2006.15969
Autor:
Pominova, Marina, Kondrateva, Ekaterina, Sharaev, Maksim, Pavlov, Sergey, Bernstein, Alexander, Burnaev, Evgeny
Deep learning convolutional neural networks have proved to be a powerful tool for MRI analysis. In current work, we explore the potential of the deformable convolutional deep neural network layers for MRI data classification. We propose new 3D deform
Externí odkaz:
http://arxiv.org/abs/1911.01898
Autor:
Pominova, Marina, Kuzina, Anna, Kondrateva, Ekaterina, Sushchinskaya, Svetlana, Sharaev, Maxim, Burnaev, Evgeny, Yarkin, and Vyacheslav
Publikováno v:
ABCD Neurocognitive Prediction Challenge, Springer LNCS, 2019
In this work, we aim at predicting children's fluid intelligence scores based on structural T1-weighted MR images from the largest long-term study of brain development and child health. The target variable was regressed on a data collection site, soc
Externí odkaz:
http://arxiv.org/abs/1905.10550
Akademický článek
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Autor:
Demchenko, Anna1 (AUTHOR) demchenkoann@yandex.ru, Kondrateva, Ekaterina1 (AUTHOR), Tabakov, Vyacheslav2 (AUTHOR), Efremova, Anna3 (AUTHOR), Salikhova, Diana3 (AUTHOR), Bukharova, Tatiana3 (AUTHOR), Goldshtein, Dmitry3 (AUTHOR), Balyasin, Maxim4 (AUTHOR), Bulatenko, Natalia3 (AUTHOR), Amelina, Elena5 (AUTHOR), Lavrov, Alexander1 (AUTHOR), Smirnikhina, Svetlana1 (AUTHOR)
Publikováno v:
International Journal of Molecular Sciences. Apr2023, Vol. 24 Issue 7, p6293. 14p.