Alexey A. Shvets

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 11 papers; trusts 518 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 98 citations into it; #6,064 of 21,575 by trust here, #7,340 by citations
• Artificial Intelligence — 75 citations into it; #13,900 of 35,345 by trust here, #16,771 by citations
• Media Technology — 36 citations into it; #2,884 of 3,621 by trust here, #2,114 by citations
• Model Architectures — 36 citations into it
• Optimization & Training — 26 citations into it
• Biophysics — 20 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Radiology, Nuclear Medicine and Imaging — 10 citations into it; #978 of 3,265 by trust here, #1,628 by citations

Whom Alexey A. Shvets trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Vladimir I. Iglovikov
2. Kaiming He
3. Karen Simonyan
4. Olaf Ronneberger
5. Alexander Rakhlin
6. Andrew Zisserman
7. Diederik P. Kingma
8. X. Zhang
9. Geoffrey E. Hinton
10. Alex Krizhevsky

Who trusts Alexey A. Shvets overall

1. Vladimir I. Iglovikov
2. Alexander Rakhlin
3. Alexander V. Buslaev
4. Selim S. Seferbekov
5. Alexandr A Kalinin
6. Artur Kuzin
7. Yauhen Babakhin
8. Paul F. Jaeger
9. S. Hasan
10. Alex Parinov

Papers from Alexey A. Shvets's perspective

Ordered by how strongly the researchers Alexey A. Shvets trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning (2018) — 388 citations
• 2017 Robotic Instrument Segmentation Challenge (2019) — 310 citations
• Medical Image Segmentation Using Deep Neural Networks with Pre-trained Encoders (2020) — 47 citations
• Breast Tumor Cellularity Assessment using Deep Neural Networks (2019) — 24 citations
• TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation (2018) — 677 citations
• Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis (2018) — 343 citations
• Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks (2018) — 75 citations
• Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery (2018) — 164 citations
• Feature Pyramid Network for Multi-class Land Segmentation (2018) — 165 citations
• TernausNetV2: Fully Convolutional Network for Instance Segmentation (2018) — 160 citations
• Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks (2017) — 128 citations