Dominic King
Artificial Intelligence · 2018–2021
A researcher in the Science corpus: 6 papers; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 92 citations into it
- Radiology, Nuclear Medicine and Imaging — 81 citations into it
Whom Dominic King trusts
Their strongest outbound trust, strongest first, inferred from their citations.
- Eric J. Topol (in Artificial Intelligence)
Papers from Dominic King's perspective
Ordered by how strongly the researchers Dominic King trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
- Rethinking the Inception Architecture for Computer Vision
- Densely Connected Convolutional Networks
- Association for Computational Linguistics " Why Should I Trust You? " Explaining the Predictions of Any Classifier
- Towards A Rigorous Science of Interpretable Machine Learning
- Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices
- Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy (Annals of Internal Medicine)
- Deep Learning for Identifying Metastatic Breast Cancer
- Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Their papers
- Clinically applicable deep learning for diagnosis and referral in retinal disease (2018) — 2,402 citations
- International evaluation of an AI system for breast cancer screening (2020) — 2,721 citations
- Key challenges for delivering clinical impact with artificial intelligence (2019) — 2,399 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