September 18

Add to Calendar 2017-09-18 16:00:00 2017-09-18 17:00:00 America/New_York Exploring Uncertainty in dMRI Super-resolution via Probabilistic CNNs Deep learning has shown success in a wide range of medical image transformation problems, such as super-resolution (SR), denoising and image synthesis. However, the highly ill-posed nature of such problems results in inevitable ambiguity in the learning of networks. In addition, the deterministic nature of the existing methods means that they provide no indication of confidence in its prediction, which hinders reliability assessment and forms a significant barrier to adoption in clinical practice.This talks will focus on our recent work which proposes a probabilistic CNN method for modelling uncertainty in image enhancement problems. We propose to account for intrinsic uncertainty through a per-patch heteroscedastic noise model and for parameter uncertainty through approximate Bayesian inference in the form of variational dropout. We show that the combined benefits of both lead to the state-of-the-art performance super-resolution of diffusion MR brain images. We further show that the method produces tangible benefits in downstream tractography. In addition, the probabilistic nature of the methods naturally confers a mechanism to quantify uncertainty over the super-resolved output. We demonstrate through experiments on both healthy and pathological brains the potential utility of such an uncertainty measure in the risk assessment of the super-resolved images for subsequent clinical use. Conference Room D451 Belfer

October 19

Add to Calendar 2017-10-19 16:00:00 2017-10-19 17:00:00 America/New_York Disease Progression Modelling of Alzheimer's Disease Subtypes Distinct neurodegenerative diseases such as typical Alzheimer'sdisease (AD) or Posterior Cortical Atrophy (PCA) exhibit differentdynamics of pathology progression in the brain. Understanding theprecise mechanisms of disease progression will enable us to createinformed drug treatments. Furthermore, quantifying the progression ofthese diseases using specialised mathematical models will enable us tostage subjects more accurately along the disease progression timeline,which is important for assessing drug efficacy. In this presentation Iwill talk about my work on developing novel disease progression modelsand their applications to typical AD and PCA. Finally, I willconclude with a few words on the TADPOLE challenge, which aims topredict the progression of individuals at risk of AD. 32-D507 Belfer

December 07

Add to Calendar 2017-12-07 16:00:00 2017-12-07 17:00:00 America/New_York Computational analysis and modeling of graph-structured neuroimaging data Graph representations are often used to model structured data at anindividual or population level and have numerous applications inpattern recognition problems. In the field of neuroscience, where suchrepresentations are commonly used to model structural or functionalconnectivity between a set of brain regions, graphs have proven to beof great importance to reveal patterns related to brain developmentand disease, which were previously unknown. This talk is going tocover the evaluation of similarity between brain connectivity networksin a manner that accounts for the graph structure and is tailored fora particular application. At the same time, exploiting the wealth ofimaging and non-imaging information for disease prediction tasksrequires models capable of simultaneously representing individualfeatures and data associations between subjects from potentially largepopulations. The latter can be particularly beneficial in large-scalestudies and graphs provide a natural framework for suchtasks. Concepts from signal processing on graphs allow convolutions ona population graph incorporating both imaging and non-imaginginformation and this talk will demonstrate their importance forsemi-supervised classification tasks, inferring subject specificproperties from their imaging features and interactions within apopulation. 32-D507 Belfer

April 19