78 research outputs found
Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification
We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL). After image enhancement and segmentation, concentric circle is used to extract the global and local features for improving the accuracy in diagnosis and prediction. The classification problem of ultrasound image is converted to sparse representation based MIL problem. Each instance of a bag is represented as a sparse linear combination of all basis vectors in the dictionary, and then the bag is represented by one feature vector which is obtained via sparse representations of all instances within the bag. The sparse and MIL problem is further converted to a conventional learning problem that is solved by relevance vector machine (RVM). Results of single classifiers are combined to be used for classification. Experimental results on the breast cancer datasets demonstrate the superiority of the proposed method in terms of classification accuracy as compared with state-of-the-art MIL methods
Modelling, Simulation and Data Analysis in Acoustical Problems
Modelling and simulation in acoustics is currently gaining importance. In fact, with the development and improvement of innovative computational techniques and with the growing need for predictive models, an impressive boost has been observed in several research and application areas, such as noise control, indoor acoustics, and industrial applications. This led us to the proposal of a special issue about “Modelling, Simulation and Data Analysis in Acoustical Problems”, as we believe in the importance of these topics in modern acoustics’ studies. In total, 81 papers were submitted and 33 of them were published, with an acceptance rate of 37.5%. According to the number of papers submitted, it can be affirmed that this is a trending topic in the scientific and academic community and this special issue will try to provide a future reference for the research that will be developed in coming years
Bifurcation analysis of the Topp model
In this paper, we study the 3-dimensional Topp model for the dynamicsof diabetes. We show that for suitable parameter values an equilibrium of this modelbifurcates through a Hopf-saddle-node bifurcation. Numerical analysis suggests thatnear this point Shilnikov homoclinic orbits exist. In addition, chaotic attractors arisethrough period doubling cascades of limit cycles.Keywords Dynamics of diabetes · Topp model · Reduced planar quartic Toppsystem · Singular point · Limit cycle · Hopf-saddle-node bifurcation · Perioddoubling bifurcation · Shilnikov homoclinic orbit · Chao
Inverse scattering and shape reconstruction.
Investigations of new and improved solutions to inverse problems are considered. Three of the solutions are concerned with inverse scattering. The other two solutions deal with reconstructing binary images from few projections and determining the shape and orientation of a three-dimensional object from silhouettes. In addition, a review of solutions to direct and inverse scattering problems is presented.
An inverse scattering algorithm for reconstructing variable refractive index distributions is examined. The inversion algorithm is based on an expression for the wave function which explicitly incorporates the inverse scattering data. It is claimed that this considerably increases the efficiency of the algorithm. The algorithm is implemented in two-dimensional space and examples of reconstructions of objects from computer-generated scattering data are presented.
The problem of determining the shape of a two-dimensional impenetrable obstacle from a set of measurements of its far-field scattering amplitude is considered. The problem is formulated as a non-linear operator equation which is solved by an iterative method. The use of the null-field method to solve the direct problem leads to efficient evaluation of the Fréchet derivative of the non-linear operator. Computational implementations confirm the numerical accuracy of the algorithm.
An extension to the Rayleigh-Gans (Born) approximation is examined. The extension involves incorporating a high frequency approximation to the wave field into the conventional Rayleigh-Gans (Born) approximation. Numerical implementation of an algorithm based on this extension to the Rayleigh-Gans (Born) approximation indicates that its reconstruction accuracy is generally superior to that of the conventional Rayleigh-Gans (Born) approximation.
Efficient algorithms for reconstructing a binary cross-section (each of whose pixel amplitudes is either zero or unity) from few one-dimensional projections are introduced and illustrated by example. It is shown that only two projections are needed to reconstruct a convex cross-section. Non-convex cross-sections need more projections but far fewer than are necessary to reconstruct grey-scale images. When presented with noisy one-dimensional projections, the algorithms remain useful, although their performance improves with the number of given projections.
Determination of a three-dimensional object's shape and orientation from its silhouettes is studied, on the understanding that the relative orientations of the given silhouettes are unknown a priori. The result of this study is an algorithm which could be suitable for incorporation into a robot's vision system. The algorithm is based on a method for determining the orientation of an object from its two-dimensional projections. To overcome the reduced information content of silhouettes as compared with two-dimensional projections, a self consistency check is introduced. Numerical implementations of the algorithm confirm that it can generate usefully accurate estimates of the orientations and shapes of technologically non-trivial objects
2nd International Conference on Numerical and Symbolic Computation
The Organizing Committee of SYMCOMP2015 – 2nd International Conference on Numerical and
Symbolic Computation: Developments and Applications welcomes all the participants and acknowledge the contribution of the authors to the success of this event.
This Second International Conference on Numerical and Symbolic Computation, is promoted by APMTAC - Associação Portuguesa de Mecânica Teórica, Aplicada e Computacional and it was organized in the context of IDMEC/IST - Instituto de Engenharia Mecânica. With this ECCOMAS
Thematic Conference it is intended to bring together academic and scientific communities that are involved with Numerical and Symbolic Computation in the most various scientific area
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Automatic model construction with Gaussian processes
This thesis develops a method for automatically constructing, visualizing and describing
a large class of models, useful for forecasting and finding structure in domains such
as time series, geological formations, and physical dynamics. These models, based on
Gaussian processes, can capture many types of statistical structure, such as periodicity,
changepoints, additivity, and symmetries. Such structure can be encoded through kernels,
which have historically been hand-chosen by experts. We show how to automate
this task, creating a system that explores an open-ended space of models and reports
the structures discovered.
To automatically construct Gaussian process models, we search over sums and products
of kernels, maximizing the approximate marginal likelihood. We show how any
model in this class can be automatically decomposed into qualitatively different parts,
and how each component can be visualized and described through text. We combine
these results into a procedure that, given a dataset, automatically constructs a model
along with a detailed report containing plots and generated text that illustrate the
structure discovered in the data.
The introductory chapters contain a tutorial showing how to express many types of
structure through kernels, and how adding and multiplying different kernels combines
their properties. Examples also show how symmetric kernels can produce priors over
topological manifolds such as cylinders, toruses, and Möbius strips, as well as their
higher-dimensional generalizations.
This thesis also explores several extensions to Gaussian process models. First, building
on existing work that relates Gaussian processes and neural nets, we analyze natural
extensions of these models to deep kernels and deep Gaussian processes. Second, we examine
additive Gaussian processes, showing their relation to the regularization method
of dropout. Third, we combine Gaussian processes with the Dirichlet process to produce
the warped mixture model: a Bayesian clustering model having nonparametric cluster
shapes, and a corresponding latent space in which each cluster has an interpretable
parametric form.This work was supported by the National Sciences and Engineering Research
Council of Canada, the Cambridge Commonwealth Trust, Pembroke College, a grant
from the Engineering and Physical Sciences Research Council, and a grant from Google
Unsupervised learning methods for identifying and evaluating disease clusters in electronic health records
Introduction
Clustering algorithms are a class of algorithms that can discover groups of observations in
complex data and are often used to identify subtypes of heterogeneous diseases in electronic
health records (EHR). Evaluating clustering experiments for biological and clinical significance is
a vital but challenging task due to the lack of consensus on best practices. As a result, the
translation of findings from clustering experiments to clinical practice is limited.
Aim
The aim of this thesis was to investigate and evaluate approaches that enable the evaluation of
clustering experiments using EHR.
Methods
We conducted a scoping review of clustering studies in EHR to identify common evaluation
approaches. We systematically investigated the performance of the identified approaches using
a cohort of Alzheimer's Disease (AD) patients as an exemplar comparing four different
clustering methods (K-means, Kernel K-means, Affinity Propagation and Latent Class
Analysis.). Using the same population, we developed and evaluated a method (MCHAMMER)
that tested whether clusterable structures exist in EHR. To develop this method we tested
several cluster validation indexes and methods of generating null data to see which are the best
at discovering clusters. In order to enable the robust benchmarking of evaluation approaches,
we created a tool that generated synthetic EHR data that contain known cluster labels across a
range of clustering scenarios.
Results
Across 67 EHR clustering studies, the most popular internal evaluation metric was comparing
cluster results across multiple algorithms (30% of studies). We examined this approach
conducting a clustering experiment on AD patients using a population of 10,065 AD patients and
21 demographic, symptom and comorbidity features. K-means found 5 clusters, Kernel K means found 2 clusters, Affinity propagation found 5 and latent class analysis found 6. K-means
4
was found to have the best clustering solution with the highest silhouette score (0.19) and was
more predictive of outcomes. The five clusters found were: typical AD (n=2026), non-typical AD
(n=1640), cardiovascular disease cluster (n=686), a cancer cluster (n=1710) and a cluster of
mental health issues, smoking and early disease onset (n=1528), which has been found in
previous research as well as in the results of other clustering methods. We created a synthetic
data generation tool which allows for the generation of realistic EHR clusters that can vary in
separation and number of noise variables to alter the difficulty of the clustering problem. We
found that decreasing cluster separation did increase cluster difficulty significantly whereas
noise variables increased cluster difficulty but not significantly. To develop the tool to assess
clusters existence we tested different methods of null dataset generation and cluster validation
indices, the best performing null dataset method was the min max method and the best
performing indices we Calinksi Harabasz index which had an accuracy of 94%, Davies Bouldin
index (97%) silhouette score ( 93%) and BWC index (90%). We further found that when clusters
were identified using the Calinski Harabasz index they were more likely to have significantly
different outcomes between clusters. Lastly we repeated the initial clustering experiment,
comparing 10 different pre-processing methods. The three best performing methods were RBF
kernel (2 clusters), MCA (4 clusters) and MCA and PCA (6 clusters). The MCA approach gave
the best results highest silhouette score (0.23) and meaningful clusters, producing 4 clusters;
heart and circulatory( n=1379), early onset mental health (n=1761), male cluster with memory
loss (n = 1823), female with more problem (n=2244).
Conclusion
We have developed and tested a series of methods and tools to enable the evaluation of EHR
clustering experiments. We developed and proposed a novel cluster evaluation metric and
provided a tool for benchmarking evaluation approaches in synthetic but realistic EHR
The resolution performance of two and three dimensional electrical impedance mammography
EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Object Recognition
Vision-based object recognition tasks are very familiar in our everyday activities, such as driving our car in the correct lane. We do these tasks effortlessly in real-time. In the last decades, with the advancement of computer technology, researchers and application developers are trying to mimic the human's capability of visually recognising. Such capability will allow machine to free human from boring or dangerous jobs
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