5,132 research outputs found
Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks
Early and accurate diagnosis of interstitial lung diseases (ILDs) is crucial
for making treatment decisions, but can be challenging even for experienced
radiologists. The diagnostic procedure is based on the detection and
recognition of the different ILD pathologies in thoracic CT scans, yet their
manifestation often appears similar. In this study, we propose the use of a
deep purely convolutional neural network for the semantic segmentation of ILD
patterns, as the basic component of a computer aided diagnosis (CAD) system for
ILDs. The proposed CNN, which consists of convolutional layers with dilated
filters, takes as input a lung CT image of arbitrary size and outputs the
corresponding label map. We trained and tested the network on a dataset of 172
sparsely annotated CT scans, within a cross-validation scheme. The training was
performed in an end-to-end and semi-supervised fashion, utilizing both labeled
and non-labeled image regions. The experimental results show significant
performance improvement with respect to the state of the art
Dynamic reconfiguration of human brain networks during learning
Human learning is a complex phenomenon requiring flexibility to adapt
existing brain function and precision in selecting new neurophysiological
activities to drive desired behavior. These two attributes -- flexibility and
selection -- must operate over multiple temporal scales as performance of a
skill changes from being slow and challenging to being fast and automatic. Such
selective adaptability is naturally provided by modular structure, which plays
a critical role in evolution, development, and optimal network function. Using
functional connectivity measurements of brain activity acquired from initial
training through mastery of a simple motor skill, we explore the role of
modularity in human learning by identifying dynamic changes of modular
organization spanning multiple temporal scales. Our results indicate that
flexibility, which we measure by the allegiance of nodes to modules, in one
experimental session predicts the relative amount of learning in a future
session. We also develop a general statistical framework for the identification
of modular architectures in evolving systems, which is broadly applicable to
disciplines where network adaptability is crucial to the understanding of
system performance.Comment: Main Text: 19 pages, 4 figures Supplementary Materials: 34 pages, 4
figures, 3 table
Changepoint Detection over Graphs with the Spectral Scan Statistic
We consider the change-point detection problem of deciding, based on noisy
measurements, whether an unknown signal over a given graph is constant or is
instead piecewise constant over two connected induced subgraphs of relatively
low cut size. We analyze the corresponding generalized likelihood ratio (GLR)
statistics and relate it to the problem of finding a sparsest cut in a graph.
We develop a tractable relaxation of the GLR statistic based on the
combinatorial Laplacian of the graph, which we call the spectral scan
statistic, and analyze its properties. We show how its performance as a testing
procedure depends directly on the spectrum of the graph, and use this result to
explicitly derive its asymptotic properties on few significant graph
topologies. Finally, we demonstrate both theoretically and by simulations that
the spectral scan statistic can outperform naive testing procedures based on
edge thresholding and testing
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