Nick Pawlowski

Microsoft Research (United Kingdom) — United Kingdom

Computer Science · Physical Sciences

17h-index1.9kcitations56works1.32yr avg
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Research Topics

Generative Adversarial Networks and Image Synthesis(10), AI in cancer detection(10), Advanced Neural Network Applications(8), Explainable Artificial Intelligence (XAI)(8), Radiomics and Machine Learning in Medical Imaging(7)

Publications56 total

Probabilistic Temporal Prediction of Continuous Disease Trajectories and Treatment Effects Using Neural SDEs
arXiv (Cornell University)·2024· 1 citedOpen Access
The Essential Role of Causality in Foundation World Models for Embodied AI
arXiv (Cornell University)·2024· 2 citedOpen Access
Probabilistic Temporal Prediction of Continuous Disease Trajectories and Treatment Effects Using Neural SDEs
Lecture notes in computer science·2024· 1 cited
Current AI applications in neurology: Brain imaging
Journal of the Neurological Sciences·2023
BayesDAG: Gradient-Based Posterior Inference for Causal Discovery
arXiv (Cornell University)·2023· 4 citedOpen Access
Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians
Nature Medicine·2023· 98 cited
High Fidelity Image Counterfactuals with Probabilistic Causal Models
arXiv (Cornell University)·2023· 4 citedOpen Access
Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models
arXiv (Cornell University)·2023· 1 citedOpen Access
Understanding Causality with Large Language Models: Feasibility and Opportunities
arXiv (Cornell University)·2023· 13 citedOpen Access
Measuring axiomatic soundness of counterfactual image models
arXiv (Cornell University)·2023· 2 citedOpen Access
Exploring Healthy Retinal Aging with Deep Learning
Ophthalmology Science·2023· 7 citedOpen Access
Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models
Lecture notes in computer science·2023· 4 cited
Deep Structural Causal Shape Models
Lecture notes in computer science·2023· 4 cited
Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians (CoDoC)
2022· 4 citedOpen Access
Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noise
arXiv (Cornell University)·2022· 3 citedOpen Access
Deep Structural Causal Shape Models
arXiv (Cornell University)·2022Open Access
NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education
arXiv (Cornell University)·2022· 1 citedOpen Access
Structured Uncertainty in the Observation Space of Variational Autoencoders
arXiv (Cornell University)·2022Open Access
Deep End-to-end Causal Inference
arXiv (Cornell University)·2022· 15 citedOpen Access
Does your dermatology classifier know what it doesn’t know? Detecting the long-tail of unseen conditions
Medical Image Analysis·2021· 16 citedOpen Access
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Frequent Co-authors

Ben Glocker(26), Daniel Rueckert(10), Martin Rajchl(9), Wenbo Gong(7), Daniel C. Castro(6), Konstantinos Kamnitsas(5), Bernhard Kainz(5), Joel Jennings(4), Joshua Durso-Finley(4), Jean-Pierre Falet(4), Douglas L. Arnold(4), Tal Arbel(4), Enzo Ferrante(3), Steven McDonagh(3), Jim Winkens(3), Shekoofeh Azizi(3), Jan Freyberg(3), Basil Mustafa(3), Vivek Natarajan(3), Greg S. Corrado(3)