7,499 research outputs found
Automated 5-year Mortality Prediction using Deep Learning and Radiomics Features from Chest Computed Tomography
We propose new methods for the prediction of 5-year mortality in elderly
individuals using chest computed tomography (CT). The methods consist of a
classifier that performs this prediction using a set of features extracted from
the CT image and segmentation maps of multiple anatomic structures. We explore
two approaches: 1) a unified framework based on deep learning, where features
and classifier are automatically learned in a single optimisation process; and
2) a multi-stage framework based on the design and selection/extraction of
hand-crafted radiomics features, followed by the classifier learning process.
Experimental results, based on a dataset of 48 annotated chest CTs, show that
the deep learning model produces a mean 5-year mortality prediction accuracy of
68.5%, while radiomics produces a mean accuracy that varies between 56% to 66%
(depending on the feature selection/extraction method and classifier). The
successful development of the proposed models has the potential to make a
profound impact in preventive and personalised healthcare.Comment: 9 page
Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database
In this paper we present a novel architecture for storing visual data.
Effective storing, browsing and searching collections of images is one of the
most important challenges of computer science. The design of architecture for
storing such data requires a set of tools and frameworks such as SQL database
management systems and service-oriented frameworks. The proposed solution is
based on a multi-layer architecture, which allows to replace any component
without recompilation of other components. The approach contains five
components, i.e. Model, Base Engine, Concrete Engine, CBIR service and
Presentation. They were based on two well-known design patterns: Dependency
Injection and Inverse of Control. For experimental purposes we implemented the
SURF local interest point detector as a feature extractor and -means
clustering as indexer. The presented architecture is intended for content-based
retrieval systems simulation purposes as well as for real-world CBIR tasks.Comment: Accepted for the 14th International Conference on Artificial
Intelligence and Soft Computing, ICAISC, June 14-18, 2015, Zakopane, Polan
The Topology ToolKit
This system paper presents the Topology ToolKit (TTK), a software platform
designed for topological data analysis in scientific visualization. TTK
provides a unified, generic, efficient, and robust implementation of key
algorithms for the topological analysis of scalar data, including: critical
points, integral lines, persistence diagrams, persistence curves, merge trees,
contour trees, Morse-Smale complexes, fiber surfaces, continuous scatterplots,
Jacobi sets, Reeb spaces, and more. TTK is easily accessible to end users due
to a tight integration with ParaView. It is also easily accessible to
developers through a variety of bindings (Python, VTK/C++) for fast prototyping
or through direct, dependence-free, C++, to ease integration into pre-existing
complex systems. While developing TTK, we faced several algorithmic and
software engineering challenges, which we document in this paper. In
particular, we present an algorithm for the construction of a discrete gradient
that complies to the critical points extracted in the piecewise-linear setting.
This algorithm guarantees a combinatorial consistency across the topological
abstractions supported by TTK, and importantly, a unified implementation of
topological data simplification for multi-scale exploration and analysis. We
also present a cached triangulation data structure, that supports time
efficient and generic traversals, which self-adjusts its memory usage on demand
for input simplicial meshes and which implicitly emulates a triangulation for
regular grids with no memory overhead. Finally, we describe an original
software architecture, which guarantees memory efficient and direct accesses to
TTK features, while still allowing for researchers powerful and easy bindings
and extensions. TTK is open source (BSD license) and its code, online
documentation and video tutorials are available on TTK's website
Pattern classification approaches for breast cancer identification via MRI: stateāofātheāart and vision for the future
Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCEMRI)
of breast tissue are discussed. The algorithms are based on recent advances in multidimensional
signal processing and aim to advance current stateāofātheāart computerāaided detection
and analysis of breast tumours when these are observed at various states of development. The topics
discussed include image feature extraction, information fusion using radiomics, multiāparametric
computerāaided classification and diagnosis using information fusion of tensorial datasets as well
as Clifford algebra based classification approaches and convolutional neural network deep learning
methodologies. The discussion also extends to semiāsupervised deep learning and selfāsupervised
strategies as well as generative adversarial networks and algorithms using generated
confrontational learning approaches. In order to address the problem of weakly labelled tumour
images, generative adversarial deep learning strategies are considered for the classification of
different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence
(AI) based framework for more robust image registration that can potentially advance the early
identification of heterogeneous tumour types, even when the associated imaged organs are
registered as separate entities embedded in more complex geometric spaces. Finally, the general
structure of a highādimensional medical imaging analysis platform that is based on multiātask
detection and learning is proposed as a way forward. The proposed algorithm makes use of novel
loss functions that form the building blocks for a generated confrontation learning methodology
that can be used for tensorial DCEāMRI. Since some of the approaches discussed are also based on
timeālapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The
proposed framework can potentially reduce the costs associated with the interpretation of medical
images by providing automated, faster and more consistent diagnosis
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