Search Results for author: Jessica Dafflon

Found 6 papers, 3 papers with code

Reliability and predictability of phenotype information from functional connectivity in large imaging datasets

1 code implementation1 May 2024 Jessica Dafflon, Dustin Moraczewski, Eric Earl, Dylan M. Nielson, Gabriel Loewinger, Patrick McClure, Adam G. Thomas, Francisco Pereira

In this study, we trained models that predict observable individual traits (phenotypes) and their corresponding singular value decomposition (SVD) representations - herein referred to as latent phenotypes from resting state functional connectivity.

Generative AI for Medical Imaging: extending the MONAI Framework

2 code implementations27 Jul 2023 Walter H. L. Pinaya, Mark S. Graham, Eric Kerfoot, Petru-Daniel Tudosiu, Jessica Dafflon, Virginia Fernandez, Pedro Sanchez, Julia Wolleb, Pedro F. da Costa, Ashay Patel, Hyungjin Chung, Can Zhao, Wei Peng, Zelong Liu, Xueyan Mei, Oeslle Lucena, Jong Chul Ye, Sotirios A. Tsaftaris, Prerna Dogra, Andrew Feng, Marc Modat, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso

We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas.

Anomaly Detection Denoising +2

Transformer-based normative modelling for anomaly detection of early schizophrenia

no code implementations8 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 implementation15 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.

Analysis of an Automated Machine Learning Approach in Brain Predictive Modelling: A data-driven approach to Predict Brain Age from Cortical Anatomical Measures

no code implementations8 Oct 2019 Jessica Dafflon, Walter H. L Pinaya, Federico Turkheimer, James H. Cole, Robert Leech, Mathew A. Harris, Simon R. Cox, Heather C. Whalley, Andrew M. McIntosh, Peter J. Hellyer

Here, we apply an autoML library called TPOT which uses a tree-based representation of machine learning pipelines and conducts a genetic-programming based approach to find the model and its hyperparameters that more closely predicts the subject's true age.

AutoML BIG-bench Machine Learning

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