10,901 research outputs found
Enhancing big data application design with the DICE framework
The focus of the DICE project is to define a quality-driven framework for developing Big data applications. DICE offers an Eclipse-based development environment, centered around a novel UML profile, to prototype, deploy, monitor, and test Big data applications. The DICE framework has been designed to natively support popular open-source solutions. The framework offers a set of 15 open source tools, which have been validated against industrial case studies in the news and media, port operations, and e-government domains
3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures
This paper analyzes the use of 3D Convolutional Neural Networks for brain
tumor segmentation in MR images. We address the problem using three different
architectures that combine fine and coarse features to obtain the final
segmentation. We compare three different networks that use multi-resolution
features in terms of both design and performance and we show that they improve
their single-resolution counterparts
An Alarm System For Segmentation Algorithm Based On Shape Model
It is usually hard for a learning system to predict correctly on rare events
that never occur in the training data, and there is no exception for
segmentation algorithms. Meanwhile, manual inspection of each case to locate
the failures becomes infeasible due to the trend of large data scale and
limited human resource. Therefore, we build an alarm system that will set off
alerts when the segmentation result is possibly unsatisfactory, assuming no
corresponding ground truth mask is provided. One plausible solution is to
project the segmentation results into a low dimensional feature space; then
learn classifiers/regressors to predict their qualities. Motivated by this, in
this paper, we learn a feature space using the shape information which is a
strong prior shared among different datasets and robust to the appearance
variation of input data.The shape feature is captured using a Variational
Auto-Encoder (VAE) network that trained with only the ground truth masks.
During testing, the segmentation results with bad shapes shall not fit the
shape prior well, resulting in large loss values. Thus, the VAE is able to
evaluate the quality of segmentation result on unseen data, without using
ground truth. Finally, we learn a regressor in the one-dimensional feature
space to predict the qualities of segmentation results. Our alarm system is
evaluated on several recent state-of-art segmentation algorithms for 3D medical
segmentation tasks. Compared with other standard quality assessment methods,
our system consistently provides more reliable prediction on the qualities of
segmentation results.Comment: Accepted to ICCV 2019 (10 pages, 4 figures
The design co-ordination framework : key elements for effective product development
This paper proposes a Design Co-ordination Framework (DCF) i.e. a concept for an ideal DC system with the abilities to support co-ordination of various complex aspects of product development. A set of frames, modelling key elements of co-ordination, which reflect the states of design, plans, organisation, allocations, tasks etc. during the design process, has been identified. Each frame is explained and the co-ordination, i.e. the management of the links between these frames, is presented, based upon characteristic DC situations in industry. It is concluded that while the DCF provides a basis for our research efforts into enhancing the product development process there is still considerable work and development required before it can adequately reflect and support Design Co-ordination
Machine learning based data mining for Milky Way filamentary structures reconstruction
We present an innovative method called FilExSeC (Filaments Extraction,
Selection and Classification), a data mining tool developed to investigate the
possibility to refine and optimize the shape reconstruction of filamentary
structures detected with a consolidated method based on the flux derivative
analysis, through the column-density maps computed from Herschel infrared
Galactic Plane Survey (Hi-GAL) observations of the Galactic plane. The present
methodology is based on a feature extraction module followed by a machine
learning model (Random Forest) dedicated to select features and to classify the
pixels of the input images. From tests on both simulations and real
observations the method appears reliable and robust with respect to the
variability of shape and distribution of filaments. In the cases of highly
defined filament structures, the presented method is able to bridge the gaps
among the detected fragments, thus improving their shape reconstruction. From a
preliminary "a posteriori" analysis of derived filament physical parameters,
the method appears potentially able to add a sufficient contribution to
complete and refine the filament reconstruction.Comment: Proceeding of WIRN 2015 Conference, May 20-22, Vietri sul Mare,
Salerno, Italy. Published in Smart Innovation, Systems and Technology,
Springer, ISSN 2190-3018, 9 pages, 4 figure
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