Mohammed M. Abdelsamea

Computer Vision and Pattern Recognition · 2014–2020

A researcher in the Science corpus: 2 papers; trusts 48 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 22 citations into it; #6,770 of 21,575 by trust here, #15,162 by citations
• Artificial Intelligence — 18 citations into it; #28,337 of 35,345 by trust here, #26,682 by citations
• Radiology, Nuclear Medicine and Imaging — 16 citations into it; #2,182 of 3,265 by trust here, #2,603 by citations
• Plant Science — 10 citations into it; #912 of 8,232 by trust here, #6,020 by citations
• Computational Biology & Bioinformatics — 7 citations into it
• Infectious Diseases — 6 citations into it

Whom Mohammed M. Abdelsamea trusts overall

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

1. Linda Wang
2. Alex Krizhevsky
3. Kaiming He
4. Christian Szegedy
5. Geoffrey E. Hinton
6. Ilya Sutskever
7. Ioannis D. Apostolopoulos
8. Pablo Arbeláez
9. Dr. Prabira Kumar Sethy
10. Arnaud Declercq

Who trusts Mohammed M. Abdelsamea overall

1. Massimo Minervini
2. Hanno Scharr
3. Jean-Michel Pape
4. Sotirios A. Tsaftaris
5. Mario Valerio Giuffrida
6. Jordan R. Ubbens
7. Christian Klukas
8. Lei Li
9. S. Suganyadevi
10. Tawsifur Rahman

Papers from Mohammed M. Abdelsamea's perspective

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

Their papers

• Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network (2020) — 917 citations
• Image-based plant phenotyping with incremental learning and active contours (2014) — 135 citations