Search Results for author: Stephen M. Smith

Found 3 papers, 3 papers with code

ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans

1 code implementation3 Mar 2021 Cher Bass, Mariana da Silva, Carole Sudre, Logan Z. J. Williams, Petru-Daniel Tudosiu, Fidel Alfaro-Almagro, Sean P. Fitzgibbon, Matthew F. Glasser, Stephen M. Smith, Emma C. Robinson

An important goal of medical imaging is to be able to precisely detect patterns of disease specific to individual scans; however, this is challenged in brain imaging by the degree of heterogeneity of shape and appearance.

Disentanglement Image Registration +1

ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping

1 code implementation NeurIPS 2020 Cher Bass, Mariana da Silva, Carole Sudre, Petru-Daniel Tudosiu, Stephen M. Smith, Emma C. Robinson

Feature attribution (FA), or the assignment of class-relevance to different locations in an image, is important for many classification problems but is particularly crucial within the neuroscience domain, where accurate mechanistic models of behaviours, or disease, require knowledge of all features discriminative of a trait.

Classification General Classification +3

Permutation Inference for Canonical Correlation Analysis

1 code implementation24 Feb 2020 Anderson M. Winkler, Olivier Renaud, Stephen M. Smith, Thomas E. Nichols

Even in the absence of nuisance variables, however, a simple permutation test for CCA also leads to excess error rates for all canonical correlations other than the first.

valid

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