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


Sep 07, 2021
Simon Dahan, Logan Z. J. Williams, Daniel Rueckert, Emma C. Robinson

* MLCN 2021 

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


Sep 06, 2021
Qiang Ma, Emma C. Robinson, Bernhard Kainz, Daniel Rueckert, Amir Alansary

* Accepted in The 4th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN2021) 

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Distinguishing Healthy Ageing from Dementia: a Biomechanical Simulation of Brain Atrophy using Deep Networks


Aug 18, 2021
Mariana Da Silva, Carole H. Sudre, Kara Garcia, Cher Bass, M. Jorge Cardoso, Emma C. Robinson

* MLCN 2021 

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Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps


Jul 06, 2021
Samuel Budd, Matthew Sinclair, Thomas Day, Athanasios Vlontzos, Jeremy Tan, Tianrui Liu, Jaqueline Matthew, Emily Skelton, John Simpson, Reza Razavi, Ben Glocker, Daniel Rueckert, Emma C. Robinson, Bernhard Kainz

* MICCAI'21 Main Conference 

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


Mar 03, 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


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Surface Agnostic Metrics for Cortical Volume Segmentation and Regression


Oct 04, 2020
Samuel Budd, Prachi Patkee, Ana Baburamani, Mary Rutherford, Emma C. Robinson, Bernhard Kainz

* Best Paper Honourable Mentions @ MLCN 2020 (MICCAI 2020) 

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ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping


Jun 16, 2020
Cher Bass, Mariana da Silva, Carole Sudre, Petru-Daniel Tudosiu, Stephen M. Smith, Emma C. Robinson

* Submitted to NeurIPS 2020: Neural Information Processing Systems. Keywords: interpretable, classification, feature attribution, domain translation, variational autoencoder, generative adversarial network, neuroimaging 

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