1 code implementation • 1 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.
no code implementations • 4 Mar 2024 • Xiaoliang Luo, Akilles Rechardt, Guangzhi Sun, Kevin K. Nejad, Felipe Yáñez, Bati Yilmaz, Kangjoo Lee, Alexandra O. Cohen, Valentina Borghesani, Anton Pashkov, Daniele Marinazzo, Jonathan Nicholas, Alessandro Salatiello, Ilia Sucholutsky, Pasquale Minervini, Sepehr Razavi, Roberta Rocca, Elkhan Yusifov, Tereza Okalova, Nianlong Gu, Martin Ferianc, Mikail Khona, Kaustubh R. Patil, Pui-Shee Lee, Rui Mata, Nicholas E. Myers, Jennifer K Bizley, Sebastian Musslick, Isil Poyraz Bilgin, Guiomar Niso, Justin M. Ales, Michael Gaebler, N Apurva Ratan Murty, Leyla Loued-Khenissi, Anna Behler, Chloe M. Hall, Jessica Dafflon, Sherry Dongqi Bao, Bradley C. Love
LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts.
2 code implementations • 27 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.
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.
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.
no code implementations • 8 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.