no code implementations • 19 Mar 2024 • Angelica I. Aviles-Rivero, Chun-Wun Cheng, Zhongying Deng, Zoe Kourtzi, Carola-Bibiane Schönlieb
Early detection of Alzheimer's disease's precursor stages is imperative for significantly enhancing patient outcomes and quality of life.
no code implementations • 18 Mar 2023 • Shujun Wang, Angelica I Aviles-Rivero, Zoe Kourtzi, Carola-Bibiane Schönlieb
We demonstrate, through extensive experiments on ADNI, that our proposed HGIB framework outperforms existing state-of-the-art hypergraph neural networks for Alzheimer's disease prognosis.
no code implementations • 2 Mar 2023 • Mahed Abroshan, Michael Burkhart, Oscar Giles, Sam Greenbury, Zoe Kourtzi, Jack Roberts, Mihaela van der Schaar, Jannetta S Steyn, Alan Wilson, May Yong
Machine learning techniques are effective for building predictive models because they identify patterns in large datasets.
no code implementations • 4 Apr 2022 • Angelica I. Aviles-Rivero, Christina Runkel, Nicolas Papadakis, Zoe Kourtzi, Carola-Bibiane Schönlieb
We demonstrate, through our experiments, that our framework is able to outperform current techniques for Alzheimer's disease diagnosis.
no code implementations • 4 Jun 2021 • Georgios Batzolis, Marcello Carioni, Christian Etmann, Soroosh Afyouni, Zoe Kourtzi, Carola Bibiane Schönlieb
We model the conditional distribution of the latent encodings by modeling the auto-regressive distributions with an efficient multi-scale normalizing flow, where each conditioning factor affects image synthesis at its respective resolution scale.