Explore projects
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Talks, practicals, and other materials for the 2020 WIN PyTreat
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Analysis pipeline used in the ‘Capturing MultiORgan Effects of COVID-19’ (C-MORE) study.
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Pipeline for unbiased multimodal template construction from T1, T2 and DTI images.
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Code for the generation, processing, and analysis of imaging confounds in UKB Biobank.
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Exploring machine learning methods, with special focus on the deep learning methods in order to attempt Neuroimage segmentation and analysis
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Pipeline for brain imaging processing of UK Biobank.
Pipeline developed by Fidel Alfaro Almagro, Steve Smith and Mark Jenkinson.
Contributions by FMRIB Analysis Group, University of Oxford.
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Tensor Image Registration Library - a modular general-purpose image registration framework for Python with prebaked scripts for histology-to-MRI registration.
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FileTrees provide a way to define the content of a structured directory. This can be very handy when defining input/output files for a pipeline.
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Code for inferring PRObabilistic FUnctional MOdes. See DOI: 10.1016/j.neuroimage.2015.01.013 and DOI: 10.1016/j.neuroimage.2020.117226.
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Computational modelling course taught as part of the WIN graduate programme.
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This project will aim to address one of the big challenges in imaging-neuroscience: that of how a brain’s functional connectivity, represented by resting-state maps, can be predicted from structural connectivity information obtained from dw-MRI.
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