collaborators

6 papers

eess.IV2020

Convolutional neural networks for automatic detection of Focal Cortical Dysplasia

Ruslan Aliev, Ekaterina Kondrateva, Maxim Sharaev +5

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…

eess.IV2020

Fader Networks for domain adaptation on fMRI: ABIDE-II study

Marina Pominova, Ekaterina Kondrateva, Maxim Sharaev +2

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 conne…

eess.IV2020

Domain Shift in Computer Vision models for MRI data analysis: An Overview

Ekaterina Kondrateva, Marina Pominova, Elena Popova +3

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…

q-bio.NC2020

Interpretation of 3D CNNs for Brain MRI Data Classification

Maxim Kan, Ruslan Aliev, Anna Rudenko +6

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, t…

eess.IV2019

3D Deformable Convolutions for MRI classification

Marina Pominova, Ekaterina Kondrateva, Maksim Sharaev +3

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 neura…

eess.IV2019

Ensemble of 3D CNN regressors with data fusion for fluid intelligence prediction

Marina Pominova, Anna Kuzina, Ekaterina Kondrateva +4

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 he…