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Boosted Training of Convolutional Neural Networks for Multi-Class Segmentation

Autor: Berger, Lorenz, Hyde, Eoin, Gibb, Matt, Pavithran, Nevil, Kelly, Garin, Mumtaz, Faiz, Ourselin, Sébastien

Training deep neural networks on large and sparse datasets is still challenging and can require large amounts of computation and memory. In this work, we address the task of performing semantic segmentation on large volumetric data sets, such as CT s

Externí odkaz: http://arxiv.org/abs/1806.05974

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An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation

Autor: Berger, Lorenz, Hyde, Eoin, Cardoso, M. Jorge, Ourselin, Sebastien

Deep convolutional neural networks (CNNs) have shown excellent performance in object recognition tasks and dense classification problems such as semantic segmentation. However, training deep neural networks on large and sparse datasets is still chall

Externí odkaz: http://arxiv.org/abs/1709.02764

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Dissertation/ Thesis

Multi-scale parameterisation of static and dynamic continuum porous perfusion models using discrete anatomical data

Autor: Hyde, Eoin Ronan

The aim of this thesis is to replace the intractable problem of using discrete flow models within large vascular networks with a suitably parameterised and tractable continuum perfusion model. Through this work, we directly address the hypothesis tha

Externí odkaz: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.627832

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Determination of complex absorbing potentials from the electron self-energy

Autor: Henderson, Thomas M., Fagas, Giorgos, Hyde, Eoin, Greer, James C.

Publikováno v: J. Chem. Phys. 125, 244104 (2006)

The electronic conductance of a molecule making contact to electrodes is determined by the coupling of discrete molecular states to the continuum electrode density of states. Interactions between bound states and continua can be modeled exactly by us

Externí odkaz: http://arxiv.org/abs/cond-mat/0611618

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