Shadi Albarqouni
Artificial Intelligence · 2016–2023
A researcher in the Science corpus: 10 papers; trusts 175 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 151 citations into it; #17,827 of 35,345 by trust here, #3,523 by citations
- Computer Vision and Pattern Recognition — 127 citations into it; #16,330 of 21,575 by trust here, #10,010 by citations
- Radiology, Nuclear Medicine and Imaging — 62 citations into it
- Generative Models — 35 citations into it
Whom Shadi Albarqouni trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- Ross B. Girshick
- Yann LeCun
- Ilya Sutskever
- Kaiming He
- Andrea Vedaldi
- Diederik P. Kingma
- Yoshua Bengio
- Jeff Donahue
Who trusts Shadi Albarqouni overall
- Massimo Salvi
- Veronika Cheplygina
- S. Suganyadevi
- Alexandru Korotcov
- Adnan Qayyum
- José Bernal
- Usha R. Acharya
- Paul Bergmann
- Christoph Baur
- Marleen de Bruijne
Papers from Shadi Albarqouni's perspective
Ordered by how strongly the researchers Shadi Albarqouni trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- ImageNet Large Scale Visual Recognition Challenge
- Attention is All you Need (Neural Information Processing Systems)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Going deeper with convolutions
Their papers
- Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer (2017) — 3,174 citations
- The future of digital health with federated learning (2020) — 2,960 citations
- Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images (2018) — 495 citations
- FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare (2023) — 491 citations
- Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study (2020) — 381 citations
- GANs for Medical Image Analysis (2018) — 505 citations
- Staingan: Stain Style Transfer for Digital Histological Images (2018) — 327 citations
- Automatic Classification of Proximal Femur Fractures Based on Attention Models (2017) — 26 citations
- Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images (2016) — 759 citations
- AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images (2016) — 502 citations