2 citations · 2 across the 5 of their papers we have counts for
7 papers
Negligible effect of brain MRI data preprocessing for tumor segmentation
Ekaterina Kondrateva, Polina Druzhinina, Alexandra Dalechina +5
Magnetic resonance imaging (MRI) data is heterogeneous due to differences in device manufacturers, scanning protocols, and inter-subject variability. A conventional way to mitigate…
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…
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…
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…
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…
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…