731,796 research outputs found
Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis
Tissue characterization has long been an important component of Computer
Aided Diagnosis (CAD) systems for automatic lesion detection and further
clinical planning. Motivated by the superior performance of deep learning
methods on various computer vision problems, there has been increasing work
applying deep learning to medical image analysis. However, the development of a
robust and reliable deep learning model for computer-aided diagnosis is still
highly challenging due to the combination of the high heterogeneity in the
medical images and the relative lack of training samples. Specifically,
annotation and labeling of the medical images is much more expensive and
time-consuming than other applications and often involves manual labor from
multiple domain experts. In this work, we propose a multi-stage, self-paced
learning framework utilizing a convolutional neural network (CNN) to classify
Computed Tomography (CT) image patches. The key contribution of this approach
is that we augment the size of training samples by refining the unlabeled
instances with a self-paced learning CNN. By implementing the framework on high
performance computing servers including the NVIDIA DGX1 machine, we obtained
the experimental result, showing that the self-pace boosted network
consistently outperformed the original network even with very scarce manual
labels. The performance gain indicates that applications with limited training
samples such as medical image analysis can benefit from using the proposed
framework.Comment: accepted by 8th International Workshop on Machine Learning in Medical
Imaging (MLMI 2017
Generalized Network Psychometrics: Combining Network and Latent Variable Models
We introduce the network model as a formal psychometric model,
conceptualizing the covariance between psychometric indicators as resulting
from pairwise interactions between observable variables in a network structure.
This contrasts with standard psychometric models, in which the covariance
between test items arises from the influence of one or more common latent
variables. Here, we present two generalizations of the network model that
encompass latent variable structures, establishing network modeling as parts of
the more general framework of Structural Equation Modeling (SEM). In the first
generalization, we model the covariance structure of latent variables as a
network. We term this framework Latent Network Modeling (LNM) and show that,
with LNM, a unique structure of conditional independence relationships between
latent variables can be obtained in an explorative manner. In the second
generalization, the residual variance-covariance structure of indicators is
modeled as a network. We term this generalization Residual Network Modeling
(RNM) and show that, within this framework, identifiable models can be obtained
in which local independence is structurally violated. These generalizations
allow for a general modeling framework that can be used to fit, and compare,
SEM models, network models, and the RNM and LNM generalizations. This
methodology has been implemented in the free-to-use software package lvnet,
which contains confirmatory model testing as well as two exploratory search
algorithms: stepwise search algorithms for low-dimensional datasets and
penalized maximum likelihood estimation for larger datasets. We show in
simulation studies that these search algorithms performs adequately in
identifying the structure of the relevant residual or latent networks. We
further demonstrate the utility of these generalizations in an empirical
example on a personality inventory dataset.Comment: Published in Psychometrik
He Ara Angitu: A Description of Literacy Achievement for Year 0 - 2 students in Total Immersion in Māori Programmes
In response to the recommendations of the Literacy Taskforce Report (1999) and issues highlighted in the Green Paper - Assessment for Success in Primary Schools (1998), the Ministry of Education funded a project in 2000 and 2001 to develop a description of achievement in reading and writing for five-year-old Māori medium students.
This provided the opportunity to take a systematic comprehensive look at children’s literacy performance during the first two years of instruction and begin to identify reasonable expectations of progress in reading, written and oral language
Strategic Management and HRM
[Excerpt] The purpose of this chapter is to discuss this intersection between Strategic Management and HRM, what we know, and future directions for SHRM research. We will begin by briefly discussing the concept of strategy and the popularization of the resource-based view (RBV) of the firm. Next we will address its role in creating the link between HRM and Strategic Management including key questions that the RBV has raised in relation to SHRM. We will then examine the current state of affairs in SHRM; the progress made, and key questions and concerns occupying the attention of SHRM researchers. Finally, we will conclude with our views on future directions for SHRM research
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