Jeffrey De Fauw

DeepMind (United Kingdom) — United Kingdom

Medicine · Health Sciences

18h-index8.7kcitations22works40.02yr avg
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Research Topics

Retinal Imaging and Analysis(6), Radiomics and Machine Learning in Medical Imaging(5), AI in cancer detection(4), Artificial Intelligence in Healthcare and Education(4), Retinal Diseases and Treatments(3)

Publications22 total

The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence
Nature Medicine·2025· 40 citedOpen Access
Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study
Journal of Medical Internet Research·2021· 316 citedOpen Access
Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol
BMJ Open·2021· 278 citedOpen Access
Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study (Preprint)
2020· 1 citedOpen Access
Addendum: International evaluation of an AI system for breast cancer screening
Nature·2020· 36 citedOpen Access
Self-Supervised MultiModal Versatile Networks
arXiv (Cornell University)·2020· 195 citedOpen Access
Self-Supervised MultiModal Versatile Networks
Neural Information Processing Systems·2020· 25 cited
Automatic segmentation using artificial intelligence of baseline anatomical parameters of patients starting anti-VEGF injections for neovascular age-related macular degeneration
2020
Quantitative analysis of change in retinal tissues in neovascular age-related macular degeneration using artificial intelligence
Investigative Ophthalmology & Visual Science·2020· 1 citedOpen Access
Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: The STARD-AI Steering Group
Nature Medicine·2020· 244 cited
Predicting conversion to wet age-related macular degeneration using deep learning
Nature Medicine·2020· 280 cited
International evaluation of an AI system for breast cancer screening
Nature·2020· 2.9k citedOpen Access
Data-Efficient Image Recognition with Contrastive Predictive Coding
arXiv (Cornell University)·2019· 936 citedOpen Access
Hierarchical Autoregressive Image Models with Auxiliary Decoders
arXiv (Cornell University)·2019· 26 citedOpen Access
Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
arXiv (Cornell University)·2018· 248 citedOpen Access
Clinically applicable deep learning for diagnosis and referral in retinal disease
Nature Medicine·2018· 2.5k cited
A Probabilistic U-Net for Segmentation of Ambiguous Images
arXiv (Cornell University)·2018· 151 citedOpen Access
Automated analysis of retinal imaging using machine learning techniques for computer vision
F1000Research·2017· 17 citedOpen Access
Applying machine learning to automated segmentation of head and neck tumour volumes and organs at risk on radiotherapy planning CT and MRI scans
F1000Research·2016· 19 citedOpen Access
Automated analysis of retinal imaging using machine learning techniques for computer vision
F1000Research·2016· 47 citedOpen Access
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Frequent Co-authors

Joseph R. Ledsam(13), Trevor Back(10), Christopher Kelly(9), Alan Karthikesalingam(8), Cían Hughes(8), Geraint Rees(8), Olaf Ronneberger(8), Dominic King(7), Bernardino Romera‐Paredes(7), Mustafa Suleyman(7), Hugh Montgomery(7), Nenad Tomašev(6), Clemens Meyer(6), Pearse A. Keane(6), Carlton Chu(6), Hutan Ashrafian(5), Ara Darzi(5), Harry Askham(5), Sander Dieleman(5), Jonathan Godwin(4)