418 research outputs found
Tensor based multichannel reconstruction for breast tumours identification from DCE-MRIs
A new methodology based on tensor algebra that uses a higher order singular value decomposition
to perform three-dimensional voxel reconstruction from a series of temporal images
obtained using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is proposed.
Principal component analysis (PCA) is used to robustly extract the spatial and temporal
image features and simultaneously de-noise the datasets. Tumour segmentation on
enhanced scaled (ES) images performed using a fuzzy C-means (FCM) cluster algorithm is
compared with that achieved using the proposed tensorial framework. The proposed algorithm
explores the correlations between spatial and temporal features in the tumours. The
multi-channel reconstruction enables improved breast tumour identification through
enhanced de-noising and improved intensity consistency. The reconstructed tumours have
clear and continuous boundaries; furthermore the reconstruction shows better voxel clustering
in tumour regions of interest. A more homogenous intensity distribution is also observed,
enabling improved image contrast between tumours and background, especially in places
where fatty tissue is imaged. The fidelity of reconstruction is further evaluated on the basis
of five new qualitative metrics. Results confirm the superiority of the tensorial approach. The
proposed reconstruction metrics should also find future applications in the assessment of
other reconstruction algorithms
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
Unsupervised Discovery of Extreme Weather Events Using Universal Representations of Emergent Organization
Spontaneous self-organization is ubiquitous in systems far from thermodynamic
equilibrium. While organized structures that emerge dominate transport
properties, universal representations that identify and describe these key
objects remain elusive. Here, we introduce a theoretically-grounded framework
for describing emergent organization that, via data-driven algorithms, is
constructive in practice. Its building blocks are spacetime lightcones that
embody how information propagates across a system through local interactions.
We show that predictive equivalence classes of lightcones -- local causal
states -- capture organized behaviors and coherent structures in complex
spatiotemporal systems. Employing an unsupervised physics-informed machine
learning algorithm and a high-performance computing implementation, we
demonstrate automatically discovering coherent structures in two real world
domain science problems. We show that local causal states identify vortices and
track their power-law decay behavior in two-dimensional fluid turbulence. We
then show how to detect and track familiar extreme weather events -- hurricanes
and atmospheric rivers -- and discover other novel coherent structures
associated with precipitation extremes in high-resolution climate data at the
grid-cell level
Dynamic Decomposition of Spatiotemporal Neural Signals
Neural signals are characterized by rich temporal and spatiotemporal dynamics
that reflect the organization of cortical networks. Theoretical research has
shown how neural networks can operate at different dynamic ranges that
correspond to specific types of information processing. Here we present a data
analysis framework that uses a linearized model of these dynamic states in
order to decompose the measured neural signal into a series of components that
capture both rhythmic and non-rhythmic neural activity. The method is based on
stochastic differential equations and Gaussian process regression. Through
computer simulations and analysis of magnetoencephalographic data, we
demonstrate the efficacy of the method in identifying meaningful modulations of
oscillatory signals corrupted by structured temporal and spatiotemporal noise.
These results suggest that the method is particularly suitable for the analysis
and interpretation of complex temporal and spatiotemporal neural signals
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