Alan Karthikesalingam

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 12 papers; trusts 30 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 306 citations into it; #35,310 of 35,345 by trust here, #3,229 by citations
• Computer Vision and Pattern Recognition — 129 citations into it
• Radiology, Nuclear Medicine and Imaging — 113 citations into it

Whom Alan Karthikesalingam trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yoshua Bengio
6. Yann LeCun
7. Ross B. Girshick

Who trusts Alan Karthikesalingam overall

1. Pranav Rajpurkar

Papers from Alan Karthikesalingam's perspective

Ordered by how strongly the researchers Alan Karthikesalingam 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Learning Multiple Layers of Features from Tiny Images
7. Gradient-based learning applied to document recognition
8. Long Short-Term Memory
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Large language models encode clinical knowledge (2022) — 4,703 citations
• Clinically applicable deep learning for diagnosis and referral in retinal disease (2018) — 2,402 citations
• Underspecification Presents Challenges for Credibility in Modern Machine Learning (2020) — 897 citations
• Big Self-Supervised Models Advance Medical Image Classification (2021) — 741 citations
• International evaluation of an AI system for breast cancer screening (2020) — 2,721 citations
• Contrastive Training for Improved Out-of-Distribution Detection (2020) — 271 citations
• Key challenges for delivering clinical impact with artificial intelligence (2019) — 2,399 citations
• Metrics Reloaded: Recommendations for image analysis validation (2022) — 479 citations
• Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis (2021) — 813 citations
• Developing Deep Learning Continuous Risk Models for Early Adverse Event Prediction in Electronic Health Records: an AKI Case Study (2019) — 3 citations
• A Clinically Applicable Approach to Continuous Prediction of Future Acute Kidney Injury (2019) — 949 citations
• Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy (2018) — 337 citations