16,807 research outputs found
NiftyNet: a deep-learning platform for medical imaging
Medical image analysis and computer-assisted intervention problems are
increasingly being addressed with deep-learning-based solutions. Established
deep-learning platforms are flexible but do not provide specific functionality
for medical image analysis and adapting them for this application requires
substantial implementation effort. Thus, there has been substantial duplication
of effort and incompatible infrastructure developed across many research
groups. This work presents the open-source NiftyNet platform for deep learning
in medical imaging. The ambition of NiftyNet is to accelerate and simplify the
development of these solutions, and to provide a common mechanism for
disseminating research outputs for the community to use, adapt and build upon.
NiftyNet provides a modular deep-learning pipeline for a range of medical
imaging applications including segmentation, regression, image generation and
representation learning applications. Components of the NiftyNet pipeline
including data loading, data augmentation, network architectures, loss
functions and evaluation metrics are tailored to, and take advantage of, the
idiosyncracies of medical image analysis and computer-assisted intervention.
NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D
and 3D images and computational graphs by default.
We present 3 illustrative medical image analysis applications built using
NiftyNet: (1) segmentation of multiple abdominal organs from computed
tomography; (2) image regression to predict computed tomography attenuation
maps from brain magnetic resonance images; and (3) generation of simulated
ultrasound images for specified anatomical poses.
NiftyNet enables researchers to rapidly develop and distribute deep learning
solutions for segmentation, regression, image generation and representation
learning applications, or extend the platform to new applications.Comment: Wenqi Li and Eli Gibson contributed equally to this work. M. Jorge
Cardoso and Tom Vercauteren contributed equally to this work. 26 pages, 6
figures; Update includes additional applications, updated author list and
formatting for journal submissio
A Development Environment for Visual Physics Analysis
The Visual Physics Analysis (VISPA) project integrates different aspects of
physics analyses into a graphical development environment. It addresses the
typical development cycle of (re-)designing, executing and verifying an
analysis. The project provides an extendable plug-in mechanism and includes
plug-ins for designing the analysis flow, for running the analysis on batch
systems, and for browsing the data content. The corresponding plug-ins are
based on an object-oriented toolkit for modular data analysis. We introduce the
main concepts of the project, describe the technical realization and
demonstrate the functionality in example applications
LEGaTO: first steps towards energy-efficient toolset for heterogeneous computing
LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.Peer ReviewedPostprint (author's final draft
Design and Implementation of Distributed Resource Management for Time Sensitive Applications
In this paper, we address distributed convergence to fair allocations of CPU
resources for time-sensitive applications. We propose a novel resource
management framework where a centralized objective for fair allocations is
decomposed into a pair of performance-driven recursive processes for updating:
(a) the allocation of computing bandwidth to the applications (resource
adaptation), executed by the resource manager, and (b) the service level of
each application (service-level adaptation), executed by each application
independently. We provide conditions under which the distributed recursive
scheme exhibits convergence to solutions of the centralized objective (i.e.,
fair allocations). Contrary to prior work on centralized optimization schemes,
the proposed framework exhibits adaptivity and robustness to changes both in
the number and nature of applications, while it assumes minimum information
available to both applications and the resource manager. We finally validate
our framework with simulations using the TrueTime toolbox in MATLAB/Simulink
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