activity
20192026
most citedICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping

9 citations · 22 across the 16 of their papers we have counts for

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Showing 2021Show all

5 papers · 1 filter

q-bio.NC2021

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

Simon Dahan, Logan Z. J. Williams, Daniel Rueckert +1

The study of functional brain connectivity (FC) is important for understanding the underlying mechanisms of many psychiatric disorders. Many recent analyses adopt graph convolution…

eess.IV20212 cited

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

Qiang Ma, Emma C. Robinson, Bernhard Kainz +2

Traditional cortical surface reconstruction is time consuming and limited by the resolution of brain Magnetic Resonance Imaging (MRI). In this work, we introduce Pial Neural Networ…

q-bio.NC2021

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

Mariana Da Silva, Carole H. Sudre, Kara Garcia +3

Biomechanical modeling of tissue deformation can be used to simulate different scenarios of longitudinal brain evolution. In this work,we present a deep learning framework for hype…

eess.IV20211 cited

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

Samuel Budd, Matthew Sinclair, Thomas Day +11

Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts. The level of skill requi…

cs.CV2021

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

Cher Bass, Mariana da Silva, Carole Sudre +7

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…