Nima Tajbakhsh

Computer Vision and Pattern Recognition · 2016–2024

A researcher in the Science corpus: 7 papers; trusts 331 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 140 citations into it; #4,383 of 21,575 by trust here, #3,191 by citations
• Artificial Intelligence — 90 citations into it
• Model Architectures — 29 citations into it
• Oncology — 12 citations into it; #210 of 1,698 by trust here, #1,139 by citations
• Biophysics — 10 citations into it
• Media Technology — 9 citations into it
• Optimization & Training — 6 citations into it

Whom Nima Tajbakhsh trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Yann LeCun
6. Ross B. Girshick
7. Diederik P. Kingma
8. Ilya Sutskever
9. Karen Simonyan
10. X. Zhang

Who trusts Nima Tajbakhsh overall

1. Deng-Ping Fan
2. Tao Zhou
3. Md Mostafijur Rahman
4. Yundong Zhang
5. Xiaoqi Zhao
6. B. Dong
7. Debesh Jha
8. Faisal Mahmood
9. Ange Lou
10. Zongwei Zhou

Papers from Nima Tajbakhsh's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• UNet++: A Nested U-Net Architecture for Medical Image Segmentation (2018) — 9,204 citations
• Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information (2016) — 1,154 citations
• UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation (2019) — 3,790 citations
• Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation (2019) — 1,007 citations
• Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? (2016) — 2,955 citations
• StarCoder 2 and The Stack v2: The Next Generation (2024) — 698 citations
• Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis (2019) — 406 citations