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A. Bernstein

12 papers hereh-index 14680 citations62 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author9
  • last author1

Across the 12 of 12 papers where every author was matched, so the position is known.

fields
  • eess.IV5
  • cs.CV4
  • cs.LG1
  • q-bio.NC1
  • stat.ML1
same name
  • A. Bernstein — 34 papers, h 45
  • A. Bernstein — 33 papers, h 29
  • A. Bernstein — 14 papers
  • A. Bernstein — 11 papers, h 25
  • A. Bernstein — 10 papers, h 52
  • A. Bernstein — 8 papers, h 25

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20122022
most citedTangent Bundle Manifold Learning via Grassmann&Stiefel Eigenmaps

27 citations · 53 across the 5 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

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…

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