Ashirbani Saha

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 245 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 90 citations into it; #15,864 of 21,575 by trust here, #15,162 by citations
• Artificial Intelligence — 51 citations into it; #28,906 of 35,345 by trust here, #23,077 by citations
• Radiology, Nuclear Medicine and Imaging — 36 citations into it; #2,239 of 3,265 by trust here, #2,073 by citations
• Neurology — 19 citations into it; #466 of 1,455 by trust here, #925 by citations
• Biomedical Engineering — 18 citations into it
• Model Architectures — 18 citations into it
• Media Technology — 5 citations into it
• Optimization & Training — 5 citations into it

Whom Ashirbani Saha trusts overall

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

1. Yoshua Bengio
2. Kaiming He
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Christian Szegedy
6. Yann LeCun
7. Ilya Sutskever
8. Karen Simonyan
9. Ross B. Girshick
10. Shaoqing Ren

Who trusts Ashirbani Saha overall

1. Ananya Kumar
2. Weihao Weng
3. Arvid Lundervold
4. Sang Michael Xie

Papers from Ashirbani Saha's perspective

Ordered by how strongly the researchers Ashirbani Saha 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm (2019) — 339 citations
• Deep learning for segmentation of brain tumors: Impact of cross‐institutional training and testing (2018) — 230 citations
• A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features (2018) — 301 citations
• Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI (2018) — 523 citations