Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET

1 code implementation • 29 Jan 2024 • Ravi Hassanaly, Camille Brianceau, Maëlys Solal, Olivier Colliot, Ninon Burgos

Over the past years, pseudo-healthy reconstruction for unsupervised anomaly detection has gained in popularity.

Image Reconstruction Unsupervised Anomaly Detection

Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET

no code implementations • 20 Nov 2023 • Maëlys Solal, Ravi Hassanaly, Ninon Burgos

Unsupervised anomaly detection is a popular approach for the analysis of neuroimaging data as it allows to identify a wide variety of anomalies from unlabelled data.

Unsupervised Anomaly Detection

Using uncertainty-aware machine learning models to study aerosol-cloud interactions

no code implementations • 30 Nov 2022 • Maëlys Solal, Andrew Jesson, Yarin Gal, Alyson Douglas

Aerosol-cloud interactions (ACI) include various effects that result from aerosols entering a cloud, and affecting cloud properties.

Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions

2 code implementations • 21 Apr 2022 • Andrew Jesson, Alyson Douglas, Peter Manshausen, Maëlys Solal, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit

Estimating the effects of continuous-valued interventions from observational data is a critically important task for climate science, healthcare, and economics.