FP7-PEOPLE-2012-IEF F.A.U.S.T. 325661

May 2012 - April 2015

F.A.U.S.T. -- "Flexible Application of Uncertainty for Scanning and Tracking"
Marie Curie Intra-European Fellowship within the 7th. European Community Framework Programme
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ClinicImppact — Clinical Intervention Modelling, Planning, and Proof for Ablation Cancer Treatment

February 2014 - January 2017

ClinicIMPPACT is an European FP7 ICT-Project. The main objective of the project is to bring the existing radio frequency ablation (RFA) model for liver cancer treatment (Project IMPPACT , Grant No. 223877, completed in February 2012) into clinical practice. I wrote Graz University of Technology's partner part of the project proposal and collaborate with the project consortium.

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iFIND: intelligent Fetal Imaging and Diagnosis

March 2014 - February 2021

Ultrasound, which passes sound waves into the body to create pictures from their reflections, is commonly used to check that babies in the womb (or fetuses) are healthy. Although every pregnant mother in the country has a scan at around 20 weeks, not all of the babies who have problems are picked up on these ultrasound scans. The iFind project is about:<br /> New technologies that allow scanning to be carried out with multiple ultrasound probes (the device which takes the ultrasound picture) at the same time which have better imaging capabilities and move automatically to the right place to get the best pictures of the whole baby. Improved fetal ultrasound imaging through automated image processing. By combining conventional ultrasound imaging from routine scans with more detailed MRI we will build a map of fetal anatomy to use for computer assisted diagnosis of fetal anomalies. These advances should mean a high quality scanning service across the country which is not dependent on local expertise, and fewer babies who have major problems will be missed.
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NVIDIA Academic Hardware Grant

February 2016 -

The following projects are currently benefiting from the NVIDIA donation of a Tesla K40 GPU computing card:
- deep learning for automatic segmentation of fetal MRI data (PhD project Amir Alansary)
- deep learning for automatic image understanding of fetal ultrasound data (MEng final year project)

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Exploration of machine learning for pre-emptive scheduling

September 2016 - August 2017

The project aims to provide and validate new approaches for machine learning in prioritised task scheduled working queues in mega-kernels executed on single instruction multiple data (SIMD) computing units. A working demonstrator using a complex real world algorithm for motion correction in fetal MRI will be used and validated on real, motion corrupted MRI data. With the proposed learning strategies, it is expected to provide accurate reconstructions of the fetal anatomy in-utero and a general framework for the parallelisation of otherwise highly complex computational methods. The fundamental GPU computing methods provide a versatile framework, which will be extended with machine learning methods to automatically and intelligently define task priorities.
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