[Demo] NiftyNet: Deep Learning platform for medical image analysis – Jorge Cardoso (UCL)

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NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy. NiftyNet aims to provide many of the tools, functionality and implementations that are essential for medical image analysis but missing from standard general purpose toolkits. Due to its modular structure, NiftyNet makes it easier to share networks and pre-trained models, adapt existing networks to new imaging data, and quickly build solutions to your own image analysis problems. This talk will explore the whys, the whats and the hows of this open source framework.

I have a BSc in Biomedical Engineering (2006) and an MSc in Medical Electronics and Signal Processing for Biomedical Engineering (2008) from the Universidade do Minho, Portugal, followed by a PhD (2008-2012) and PostDoc (2012-2015) in medical image analysis, machine learning and biomarker development between CMIC and the Dementia Research Centre at UCL. In June 2015 I have been appointed Lecturer in Quantitative Neuroradiology at the Translational Imaging Group, part of CMIC, in collaboration with the National Hospital for Neurology and Neurosurgery, working on developing, translating and integrating artificial intelligence-based quantitative imaging biomarkers into the clinical environment.

Comments

RUKUNDO PAUL says:

Congratulations for your great work! This is so interesting!

Bhuvanesh s.k says:

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