Explore Brain Ageing Pattern in MRI with Deep Learning
Date:
02 Mar, 2021 (Tue)
Time:
4:00 pm
Webinar Link:
https://hku.zoom.us/j/98227475893

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Speaker

Dr. Han Peng
Visual Geometry Group (VGG)
and Centre for Functional MRI of the Brain (WIN-FMRIB)
University of Oxford

Abstract

Convolution neural network has huge potential for accurate disease prediction with neuroimaging data, but the popular ImageNet models are often not transferable to medical data due to the training-dataset size and computing memory requirements. To address this, we propose a deep convolutional neural network model, Simple Fully Convolutional Network (SFCN), benchmarked with brain age prediction using 3D T1-weighted structural MRI data. Compared with other popular deep network architectures, SFCN has fewer parameters, so is more compatible with small dataset size and 3D volume data. We compared our SFCN approach with several widely-used machine learning models. It achieved state-of-the-art performance in UK Biobank brain imaging data (N = 14,503), with mean absolute error (MAE) = 2.14 years in brain age prediction and 99.5% in sex classification. SFCN also won the 2019 Predictive Analysis Challenge for brain age prediction, involving 79 competing teams (N = 2,638, MAE = 2.90y).

In this talk, Dr. Peng will introduce the design and the optimisation of SFCN, as well as the on-going work in brain ageing pattern discovery by interpreting internal activations of convnet predictive models using the large UK Biobank brain imaging dataset.
  

Biography of the speaker:

Han Peng is a postdoctoral researcher at University of Oxford, working jointly at the MR image analysis group at WIN-FMRIB and the computer vision group – VGG, advised by Prof Stephen M. Smith and Prof Andrea Vedaldi. His research focuses on developing deep learning methods to discover disease patterns in large MRI datasets, especially utilizing the world’s largest brain imaging dataset, UK Biobank. Han received his PhD in Physics from Oxford in 2018 in condensed matter experiment and scientific data analysis.


Organizer

Prof. Ed Wu

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