Zobrazeno 1 - 10
of 71
pro vyhledávání: '"Sharaev, Maxim"'
Publikováno v:
In Cognitive Systems Research June 2024 85
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
Publikováno v:
Journal of Cognitive Science, 2020
In this paper, our focus is the connection and influence of language technologies on the research in neurolinguistics. We present a review of brain imaging-based neurolinguistic studies with a focus on the natural language representations, such as wo
Externí odkaz:
http://arxiv.org/abs/2003.10540
Autor:
Bouzid, Amal, Almidani, Abdulrahman, Zubrikhina, Maria, Kamzanova, Altyngul, Ilce, Burcu Yener, Zholdassova, Manzura, Yusuf, Ayesha M., Bhamidimarri, Poorna Manasa, AlHaj, Hamid A., Kustubayeva, Almira, Bernstein, Alexander, Burnaev, Evgeny, Sharaev, Maxim, Hamoudi, Rifat
Publikováno v:
In Neurobiology of Stress September 2023 26
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
Autor:
Boyko Maria, Antipushina Ekaterina, Bernstein Alexander, Sharaev Maxim, Apanovich Natalya, Matveev Vsevolod, Alferova Vera, Matveev Alexey
Publikováno v:
BIO Web of Conferences, Vol 100, p 01009 (2024)
Kidney cancer has a high metastatic potential with up to 30% of patients developing distant metastasis after surgery. We assessed the value of AI models in predicting the metastatic potential of clear cell renal cell carcinoma (ccRCC), based on the g
Externí odkaz:
https://doaj.org/article/bd492b02e3d44d59b39f086d7511086d
Autor:
Sharaev, Maxim, Andreev, Alexander, Artemov, Alexey, Bernstein, Alexander, Burnaev, Evgeny, Kondratyeva, Ekaterina, Sushchinskaya, Svetlana, Akzhigitov, Renat
As machine learning continues to gain momentum in the neuroscience community, we witness the emergence of novel applications such as diagnostics, characterization, and treatment outcome prediction for psychiatric and neurological disorders, for insta
Externí odkaz:
http://arxiv.org/abs/1804.10167