411 research outputs found
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Pattern classification approaches for breast cancer identification via MRI: stateâofâtheâart 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 stateâofâtheâart computerâaided 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, multiâparametric
computerâaided 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 semiâsupervised deep learning and selfâsupervised
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 highâdimensional medical imaging analysis platform that is based on multiâtask
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 DCEâMRI. Since some of the approaches discussed are also based on
timeâlapse 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
GD-CAF: Graph Dual-stream Convolutional Attention Fusion for Precipitation Nowcasting
Accurate precipitation nowcasting is essential for various applications,
including flood prediction, disaster management, optimizing agricultural
activities, managing transportation routes and renewable energy. While several
studies have addressed this challenging task from a sequence-to-sequence
perspective, most of them have focused on a single area without considering the
existing correlation between multiple disjoint regions. In this paper, we
formulate precipitation nowcasting as a spatiotemporal graph sequence
nowcasting problem. In particular, we introduce Graph Dual-stream Convolutional
Attention Fusion (GD-CAF), a novel approach designed to learn from historical
spatiotemporal graph of precipitation maps and nowcast future time step ahead
precipitation at different spatial locations. GD-CAF consists of
spatio-temporal convolutional attention as well as gated fusion modules which
are equipped with depthwise-separable convolutional operations. This
enhancement enables the model to directly process the high-dimensional
spatiotemporal graph of precipitation maps and exploits higher-order
correlations between the data dimensions. We evaluate our model on seven years
of precipitation maps across Europe and its neighboring areas collected from
the ERA5 dataset, provided by Copernicus Climate Change Services. The
experimental results reveal the superior performance of the GD-CAF model
compared to the other examined models. Additionally, visualizations of averaged
seasonal spatial and temporal attention scores across the test set offer
valuable insights into the most robust connections between diverse regions or
time steps.Comment: 19 pages, 13 figure
Multiscale approach including microfibril scale to assess elastic constants of cortical bone based on neural network computation and homogenization method
The complexity and heterogeneity of bone tissue require a multiscale
modelling to understand its mechanical behaviour and its remodelling
mechanisms. In this paper, a novel multiscale hierarchical approach including
microfibril scale based on hybrid neural network computation and homogenisation
equations was developed to link nanoscopic and macroscopic scales to estimate
the elastic properties of human cortical bone. The multiscale model is divided
into three main phases: (i) in step 0, the elastic constants of collagen-water
and mineral-water composites are calculated by averaging the upper and lower
Hill bounds; (ii) in step 1, the elastic properties of the collagen microfibril
are computed using a trained neural network simulation. Finite element (FE)
calculation is performed at nanoscopic levels to provide a database to train an
in-house neural network program; (iii) in steps 2 to 10 from fibril to
continuum cortical bone tissue, homogenisation equations are used to perform
the computation at the higher scales. The neural network outputs (elastic
properties of the microfibril) are used as inputs for the homogenisation
computation to determine the properties of mineralised collagen fibril. The
mechanical and geometrical properties of bone constituents (mineral, collagen
and cross-links) as well as the porosity were taken in consideration. This
paper aims to predict analytically the effective elastic constants of cortical
bone by modelling its elastic response at these different scales, ranging from
the nanostructural to mesostructural levels. Our findings of the lowest scale's
output were well integrated with the other higher levels and serve as inputs
for the next higher scale modelling. Good agreement was obtained between our
predicted results and literature data.Comment: 2
Electronic systems for the restoration of the sense of touch in upper limb prosthetics
In the last few years, research on active prosthetics for upper limbs focused
on improving the human functionalities and the control. New methods have
been proposed for measuring the user muscle activity and translating it into
the prosthesis control commands. Developing the feed-forward interface so
that the prosthesis better follows the intention of the user is an important
step towards improving the quality of life of people with limb amputation.
However, prosthesis users can neither feel if something or someone is
touching them over the prosthesis and nor perceive the temperature or
roughness of objects. Prosthesis users are helped by looking at an object,
but they cannot detect anything otherwise. Their sight gives them most
information. Therefore, to foster the prosthesis embodiment and utility,
it is necessary to have a prosthetic system that not only responds to the
control signals provided by the user, but also transmits back to the user
the information about the current state of the prosthesis.
This thesis presents an electronic skin system to close the loop in prostheses
towards the restoration of the sense of touch in prosthesis users. The
proposed electronic skin system inlcudes an advanced distributed sensing
(electronic skin), a system for (i) signal conditioning, (ii) data acquisition,
and (iii) data processing, and a stimulation system. The idea is to integrate
all these components into a myoelectric prosthesis.
Embedding the electronic system and the sensing materials is a critical issue
on the way of development of new prostheses. In particular, processing
the data, originated from the electronic skin, into low- or high-level information
is the key issue to be addressed by the embedded electronic system.
Recently, it has been proved that the Machine Learning is a promising
approach in processing tactile sensors information. Many studies have
been shown the Machine Learning eectiveness in the classication of input
touch modalities.More specically, this thesis is focused on the stimulation system, allowing
the communication of a mechanical interaction from the electronic skin
to prosthesis users, and the dedicated implementation of algorithms for
processing tactile data originating from the electronic skin. On system
level, the thesis provides design of the experimental setup, experimental
protocol, and of algorithms to process tactile data. On architectural level,
the thesis proposes a design
ow for the implementation of digital circuits
for both FPGA and integrated circuits, and techniques for the power
management of embedded systems for Machine Learning algorithms
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The role of HG in the analysis of temporal iteration and interaural correlation
Mild cognitive impairment and fMRI studies of brain functional connectivity: the state of the art
In the last 15 years, many articles have studied brain connectivity in Mild Cognitive Impairment patients with fMRI techniques, seemingly using different connectivity statistical models in each investigation to identify complex connectivity structures so as to recognize typical behavior in this type of patient. This diversity in statistical approaches may cause problems in results comparison. This paper seeks to describe how researchers approached the study of brain connectivity in MCI patients using fMRI techniques from 2002 to 2014. The focus is on the statistical analysis proposed by each research group in reference to the limitations and possibilities of those techniques to identify some recommendations to improve the study of functional connectivity. The included articles came from a search of Web of Science and PsycINFO using the following keywords: f MRI, MCI, and functional connectivity. Eighty-one papers were found, but two of them were discarded because of the lack of statistical analysis. Accordingly, 79 articles were included in this review
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