2,248 research outputs found
Recommended from our members
Developing web services in a computational grid environment
Grid and Web services are both hot topics today. In this paper, we will present some ongoing work and planned future work at the Cambridge eScience Centre. After an introduction to these technologies in the context of Grid applications development, we describe two use-cases: a database
of results in computational fluid dynamics (CFD) and a small computational Grid for aircraft engineering design.
As Grid services are moving towards Web services, we continue
to make use of the Globus Toolkit v2.4 (GT2.4), without
adopting the Open Grid Services Architecture (OGSA)
wholesale. In our scenario, GT2.4 integrates distributed
computing resources including HPC and clusters while Web
services wrap the scientific code as a service.Proceedings of the IEEE International Conference on Services Computing, Shanghai, September 200
Emergency TeleOrthoPaedics m-health system for wireless communication links
For the first time, a complete wireless and mobile emergency TeleOrthoPaedics system with field trials and expert opinion is presented. The system enables doctors in a remote area to obtain a second opinion from doctors in the hospital using secured wireless telecommunication networks. Doctors can exchange securely medical images and video as well as other important data, and thus perform remote consultations, fast and accurately using a user friendly interface, via a reliable and secure telemedicine system of low cost. The quality of the transmitted compressed (JPEG2000) images was measured using different metrics and doctors opinions. The results have shown that all metrics were within acceptable limits. The performance of the system was evaluated successfully under different wireless communication links based on real data
Global Grids and Software Toolkits: A Study of Four Grid Middleware Technologies
Grid is an infrastructure that involves the integrated and collaborative use
of computers, networks, databases and scientific instruments owned and managed
by multiple organizations. Grid applications often involve large amounts of
data and/or computing resources that require secure resource sharing across
organizational boundaries. This makes Grid application management and
deployment a complex undertaking. Grid middlewares provide users with seamless
computing ability and uniform access to resources in the heterogeneous Grid
environment. Several software toolkits and systems have been developed, most of
which are results of academic research projects, all over the world. This
chapter will focus on four of these middlewares--UNICORE, Globus, Legion and
Gridbus. It also presents our implementation of a resource broker for UNICORE
as this functionality was not supported in it. A comparison of these systems on
the basis of the architecture, implementation model and several other features
is included.Comment: 19 pages, 10 figure
Space-Varying Coefficient Models for Brain Imaging
The methodological development and the application in this paper originate from diffusion tensor imaging (DTI), a powerful nuclear magnetic resonance technique enabling diagnosis and monitoring of several diseases as well as reconstruction of neural pathways. We reformulate the current analysis framework of separate voxelwise regressions as a 3d space-varying coefficient model (VCM) for the entire set of DTI images recorded on a 3d grid of voxels. Hence by allowing to borrow strength from spatially adjacent voxels, to smooth noisy observations, and to estimate diffusion tensors at any location within the brain, the three-step cascade of standard data processing is overcome simultaneously. We conceptualize two VCM variants based on B-spline basis functions: a full tensor product approach and a sequential approximation, rendering the VCM numerically and computationally feasible even for the huge dimension of the joint model in a realistic setup. A simulation study shows that both approaches outperform the standard method of voxelwise regressions with subsequent regularization. Due to major efficacy, we apply the sequential method to a clinical DTI data set and demonstrate the inherent ability of increasing the rigid grid resolution by evaluating the incorporated basis functions at intermediate points. In conclusion, the suggested fitting methods clearly improve the current state-of-the-art, but ameloriation of local adaptivity remains desirable
Unsupervised augmentation optimization for few-shot medical image segmentation
The augmentation parameters matter to few-shot semantic segmentation since
they directly affect the training outcome by feeding the networks with varying
perturbated samples. However, searching optimal augmentation parameters for
few-shot segmentation models without annotations is a challenge that current
methods fail to address. In this paper, we first propose a framework to
determine the ``optimal'' parameters without human annotations by solving a
distribution-matching problem between the intra-instance and intra-class
similarity distribution, with the intra-instance similarity describing the
similarity between the original sample of a particular anatomy and its
augmented ones and the intra-class similarity representing the similarity
between the selected sample and the others in the same class. Extensive
experiments demonstrate the superiority of our optimized augmentation in
boosting few-shot segmentation models. We greatly improve the top competing
method by 1.27\% and 1.11\% on Abd-MRI and Abd-CT datasets, respectively, and
even achieve a significant improvement for SSL-ALP on the left kidney by 3.39\%
on the Abd-CT dataset
Attentive Symmetric Autoencoder for Brain MRI Segmentation
Self-supervised learning methods based on image patch reconstruction have
witnessed great success in training auto-encoders, whose pre-trained weights
can be transferred to fine-tune other downstream tasks of image understanding.
However, existing methods seldom study the various importance of reconstructed
patches and the symmetry of anatomical structures, when they are applied to 3D
medical images. In this paper we propose a novel Attentive Symmetric
Auto-encoder (ASA) based on Vision Transformer (ViT) for 3D brain MRI
segmentation tasks. We conjecture that forcing the auto-encoder to recover
informative image regions can harvest more discriminative representations, than
to recover smooth image patches. Then we adopt a gradient based metric to
estimate the importance of each image patch. In the pre-training stage, the
proposed auto-encoder pays more attention to reconstruct the informative
patches according to the gradient metrics. Moreover, we resort to the prior of
brain structures and develop a Symmetric Position Encoding (SPE) method to
better exploit the correlations between long-range but spatially symmetric
regions to obtain effective features. Experimental results show that our
proposed attentive symmetric auto-encoder outperforms the state-of-the-art
self-supervised learning methods and medical image segmentation models on three
brain MRI segmentation benchmarks.Comment: MICCAI 2022, code:https://github.com/lhaof/AS
Ergonomics of the Operative Field in Paediatric Minimal Access Surgery
Imperial Users onl
- …