12,576 research outputs found
A Compact Representation of Histopathology Images using Digital Stain Separation & Frequency-Based Encoded Local Projections
In recent years, histopathology images have been increasingly used as a
diagnostic tool in the medical field. The process of accurately diagnosing a
biopsy sample requires significant expertise in the field, and as such can be
time-consuming and is prone to uncertainty and error. With the advent of
digital pathology, using image recognition systems to highlight problem areas
or locate similar images can aid pathologists in making quick and accurate
diagnoses. In this paper, we specifically consider the encoded local
projections (ELP) algorithm, which has previously shown some success as a tool
for classification and recognition of histopathology images. We build on the
success of the ELP algorithm as a means for image classification and
recognition by proposing a modified algorithm which captures the local
frequency information of the image. The proposed algorithm estimates local
frequencies by quantifying the changes in multiple projections in local windows
of greyscale images. By doing so we remove the need to store the full
projections, thus significantly reducing the histogram size, and decreasing
computation time for image retrieval and classification tasks. Furthermore, we
investigate the effectiveness of applying our method to histopathology images
which have been digitally separated into their hematoxylin and eosin stain
components. The proposed algorithm is tested on the publicly available invasive
ductal carcinoma (IDC) data set. The histograms are used to train an SVM to
classify the data. The experiments showed that the proposed method outperforms
the original ELP algorithm in image retrieval tasks. On classification tasks,
the results are found to be comparable to state-of-the-art deep learning
methods and better than many handcrafted features from the literature.Comment: Accepted for publication in the International Conference on Image
Analysis and Recognition (ICIAR 2019
Scalable iterative methods for sampling from massive Gaussian random vectors
Sampling from Gaussian Markov random fields (GMRFs), that is multivariate
Gaussian ran- dom vectors that are parameterised by the inverse of their
covariance matrix, is a fundamental problem in computational statistics. In
this paper, we show how we can exploit arbitrarily accu- rate approximations to
a GMRF to speed up Krylov subspace sampling methods. We also show that these
methods can be used when computing the normalising constant of a large
multivariate Gaussian distribution, which is needed for both any
likelihood-based inference method. The method we derive is also applicable to
other structured Gaussian random vectors and, in particu- lar, we show that
when the precision matrix is a perturbation of a (block) circulant matrix, it
is still possible to derive O(n log n) sampling schemes.Comment: 17 Pages, 4 Figure
Improving information centrality of a node in complex networks by adding edges
The problem of increasing the centrality of a network node arises in many
practical applications. In this paper, we study the optimization problem of
maximizing the information centrality of a given node in a network
with nodes and edges, by creating new edges incident to . Since
is the reciprocal of the sum of resistance distance
between and all nodes, we alternatively consider the problem of minimizing
by adding new edges linked to . We show that the
objective function is monotone and supermodular. We provide a simple greedy
algorithm with an approximation factor and
running time. To speed up the computation, we also present an
algorithm to compute -approximate
resistance distance after iteratively adding edges, the
running time of which is for any
, where the notation suppresses the factors. We experimentally demonstrate the effectiveness and
efficiency of our proposed algorithms.Comment: 7 pages, 2 figures, ijcai-201
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