Transformer-based normative modelling for anomaly detection of early schizophrenia

no code implementations • 8 Dec 2022 • Pedro F Da Costa, Jessica Dafflon, Sergio Leonardo Mendes, João Ricardo Sato, M. Jorge Cardoso, Robert Leech, Emily JH Jones, Walter H. L. Pinaya

Using the predicted likelihood of the scans as a proxy for a normative score, we obtained an AUROC of 0. 82 when assessing the difference between controls and individuals with early-stage schizophrenia.

Anomaly Detection

Brain Imaging Generation with Latent Diffusion Models

1 code implementation • 15 Sep 2022 • Walter H. L. Pinaya, Petru-Daniel Tudosiu, Jessica Dafflon, Pedro F Da Costa, Virginia Fernandez, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso

In this study, we explore using Latent Diffusion Models to generate synthetic images from high-resolution 3D brain images.

Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models

no code implementations • 7 Jun 2022 • Walter H. L. Pinaya, Mark S. Graham, Robert Gray, Pedro F Da Costa, Petru-Daniel Tudosiu, Paul Wright, Yee H. Mah, Andrew D. MacKinnon, James T. Teo, Rolf Jager, David Werring, Geraint Rees, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso

Deep generative models have emerged as promising tools for detecting arbitrary anomalies in data, dispensing with the necessity for manual labelling.

Anomaly Detection Denoising