no code implementations • 8 Aug 2022 • Margherita Rosnati, Eyal Soreq, Miguel Monteiro, Lucia Li, Neil S. N. Graham, Karl Zimmerman, Carlotta Rossi, Greta Carrara, Guido Bertolini, David J. Sharp, Ben Glocker
We compare the predictive power of our proposed features to the Marshall score, independently and when paired with classic TBI biomarkers.
no code implementations • 27 Jun 2022 • Sebastian G. Popescu, David J. Sharp, James H. Cole, Konstantinos Kamnitsas, Ben Glocker
Moreover, by applying the same segmentation model to out-of-distribution data (i. e., images with pathology such as brain tumors), we show that our uncertainty estimates result in out-of-distribution detection that outperforms the capabilities of previous Bayesian networks and reconstruction-based approaches that learn normative distributions.
no code implementations • 29 Sep 2021 • Honglin Li, Frieder Ganz, David J. Sharp, Payam M. Barnaghi
The proposed model can continually learn and embed new tasks into the model without losing the information about previously learned tasks.
no code implementations • 29 Sep 2021 • Alexander Capstick, Samaneh Kouchaki, Mazdak Ghajari, David J. Sharp, Payam M. Barnaghi
Recurrent deep learning methods have a larger capacity for learning complex representations in time series data.
1 code implementation • 14 May 2021 • Roonak Rezvani, Samaneh Kouchaki, Ramin Nilforooshan, David J. Sharp, Payam Barnaghi
We train and test the proposed model on a dataset from a clinical study.
no code implementations • 28 Apr 2021 • Sebastian G. Popescu, David J. Sharp, James H. Cole, Konstantinos Kamnitsas, Ben Glocker
We propose a parameter efficient Bayesian layer for hierarchical convolutional Gaussian Processes that incorporates Gaussian Processes operating in Wasserstein-2 space to reliably propagate uncertainty.
1 code implementation • 18 Jan 2021 • Honglin Li, Roonak Rezvani, Magdalena Anita Kolanko, David J. Sharp, Maitreyee Wairagkar, Ravi Vaidyanathan, Ramin Nilforooshan, Payam Barnaghi
We have developed an integrated platform to collect in-home sensor data and performed an observational study to apply machine learning models for agitation and UTI risk analysis.
no code implementations • 27 Nov 2020 • Honglin Li, Magdalena Anita Kolanko, Shirin Enshaeifar, Severin Skillman, Andreas Markides, Mark Kenny, Eyal Soreq, Samaneh Kouchaki, Kirsten Jensen, Loren Cameron, Michael Crone, Paul Freemont, Helen Rostill, David J. Sharp, Ramin Nilforooshan, Payam Barnaghi
Machine learning techniques combined with in-home monitoring technologies provide a unique opportunity to automate diagnosis and early detection of adverse health conditions in long-term conditions such as dementia.
no code implementations • pproximateinference AABI Symposium 2021 • Sebastian Popescu, David J. Sharp, James H. Cole, Ben Glocker
We propose a decoupling in Reproducing Kernel Hilbert Space of the parametric and non-parametric components of Sparse Gaussian Processes.