Search Results for author: Emma C. Robinson

Found 13 papers, 8 papers with code

Surface Analysis with Vision Transformers

1 code implementation31 May 2022 Simon Dahan, Logan Z. J. Williams, Abdulah Fawaz, Daniel Rueckert, Emma C. Robinson

The extension of convolutional neural networks (CNNs) to non-Euclidean geometries has led to multiple frameworks for studying manifolds.

Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces

1 code implementation7 Apr 2022 Simon Dahan, Hao Xu, Logan Z. J. Williams, Abdulah Fawaz, Chunhui Yang, Timothy S. Coalson, Michelle C. Williams, David E. Newby, A. David Edwards, Matthew F. Glasser, Alistair A. Young, Daniel Rueckert, Emma C. Robinson

Results suggest that Surface Vision Transformers (SiT) demonstrate consistent improvement over geometric deep learning methods for brain age and fluid intelligence prediction and achieve comparable performance on calcium score classification to standard metrics used in clinical practice.

Classification Data Augmentation

Surface Vision Transformers: Attention-Based Modelling applied to Cortical Analysis

1 code implementation30 Mar 2022 Simon Dahan, Abdulah Fawaz, Logan Z. J. Williams, Chunhui Yang, Timothy S. Coalson, Matthew F. Glasser, A. David Edwards, Daniel Rueckert, Emma C. Robinson

Motivated by the success of attention-modelling in computer vision, we translate convolution-free vision transformer approaches to surface data, to introduce a domain-agnostic architecture to study any surface data projected onto a spherical manifold.

A Deep-Discrete Learning Framework for Spherical Surface Registration

no code implementations24 Mar 2022 Mohamed A. Suliman, Logan Z. J. Williams, Abdulah Fawaz, Emma C. Robinson

Cortical surface registration is a fundamental tool for neuroimaging analysis that has been shown to improve the alignment of functional regions relative to volumetric approaches.

Image Registration Multi-Label Classification

CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs

1 code implementation16 Feb 2022 Qiang Ma, Liu Li, Emma C. Robinson, Bernhard Kainz, Daniel Rueckert, Amir Alansary

Following the isosurface extraction step, two CortexODE models are trained to deform the initial surface to white matter and pial surfaces respectively.

Surface Reconstruction

Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity

1 code implementation7 Sep 2021 Simon Dahan, Logan Z. J. Williams, Daniel Rueckert, Emma C. Robinson

Results show a prediction accuracy of 94. 4% for sex classification (an increase of 6. 2% compared to other methods), and an improvement of correlation with fluid intelligence of 0. 325 vs 0. 144, relative to a baseline model that encodes space and time separately.

Action Recognition Skeleton Based Action Recognition

PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction

1 code implementation6 Sep 2021 Qiang Ma, Emma C. Robinson, Bernhard Kainz, Daniel Rueckert, Amir Alansary

Traditional cortical surface reconstruction is time consuming and limited by the resolution of brain Magnetic Resonance Imaging (MRI).

Surface Reconstruction

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

Surface Agnostic Metrics for Cortical Volume Segmentation and Regression

no code implementations4 Oct 2020 Samuel Budd, Prachi Patkee, Ana Baburamani, Mary Rutherford, Emma C. Robinson, Bernhard Kainz

The cerebral cortex performs higher-order brain functions and is thus implicated in a range of cognitive disorders.

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

A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis

no code implementations7 Oct 2019 Samuel Budd, Emma C. Robinson, Bernhard Kainz

Fully automatic deep learning has become the state-of-the-art technique for many tasks including image acquisition, analysis and interpretation, and for the extraction of clinically useful information for computer-aided detection, diagnosis, treatment planning, intervention and therapy.

Active Learning

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