19,530 research outputs found
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Landsat Satellite Image Segmentation Using the Fuzzy ARTMAP Neural Network
This application illustrates how the fuzzy ARTMAP neural network can be used to monitor environmental changes. A benchmark problem seeks to classify regions of a Landsat image into six soil and crop classes based on images from four spectral sensors. Simulations show that fuzzy ARTMAP outperforms fourteen other neural network and machine learning algorithms. Only the k-Nearest-Neighbor algorithm shows better performance (91% vs. 89%) but without any code compression, while fuzzy ARTMAP achieves a code compression ratio of 6:1. Even with a code compression ratio of 50:1 fuzzy ARTMAP still maintains good performance (83%). This example shows how fuzzy ARTMAP can combine accuracy and code compression in real-world applications.Office of Naval Research (N00014-92-J-401J, N00014-91-J-4100, N00014-92-J-4015); National Science Foundation (IRI 90-00530
Landsat Satellite Image Segmentation Using the Fuzzy ARTMAP Neural Network
This application illustrates how the fuzzy ARTMAP neural network can be used to monitor environmental changes. A benchmark problem seeks to classify regions of a Landsat image into six soil and crop classes based on images from four spectral sensors. Simulations show that fuzzy ARTMAP outperforms fourteen other neural network and machine learning algorithms. Only the k-Nearest-Neighbor algorithm shows better performance (91% vs. 89%) but without any code compression, while fuzzy ARTMAP achieves a code compression ratio of 6:1. Even with a code compression ratio of 50:1 fuzzy ARTMAP still maintains good performance (83%). This example shows how fuzzy ARTMAP can combine accuracy and code compression in real-world applications.Office of Naval Research (N00014-92-J-401J, N00014-91-J-4100, N00014-92-J-4015); National Science Foundation (IRI 90-00530
Efficient video indexing for monitoring disease activity and progression in the upper gastrointestinal tract
Endoscopy is a routine imaging technique used for both diagnosis and
minimally invasive surgical treatment. While the endoscopy video contains a
wealth of information, tools to capture this information for the purpose of
clinical reporting are rather poor. In date, endoscopists do not have any
access to tools that enable them to browse the video data in an efficient and
user friendly manner. Fast and reliable video retrieval methods could for
example, allow them to review data from previous exams and therefore improve
their ability to monitor disease progression. Deep learning provides new
avenues of compressing and indexing video in an extremely efficient manner. In
this study, we propose to use an autoencoder for efficient video compression
and fast retrieval of video images. To boost the accuracy of video image
retrieval and to address data variability like multi-modality and view-point
changes, we propose the integration of a Siamese network. We demonstrate that
our approach is competitive in retrieving images from 3 large scale videos of 3
different patients obtained against the query samples of their previous
diagnosis. Quantitative validation shows that the combined approach yield an
overall improvement of 5% and 8% over classical and variational autoencoders,
respectively.Comment: Accepted at IEEE International Symposium on Biomedical Imaging
(ISBI), 201
Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation
Convolutional neural networks have been widely deployed in various
application scenarios. In order to extend the applications' boundaries to some
accuracy-crucial domains, researchers have been investigating approaches to
boost accuracy through either deeper or wider network structures, which brings
with them the exponential increment of the computational and storage cost,
delaying the responding time. In this paper, we propose a general training
framework named self distillation, which notably enhances the performance
(accuracy) of convolutional neural networks through shrinking the size of the
network rather than aggrandizing it. Different from traditional knowledge
distillation - a knowledge transformation methodology among networks, which
forces student neural networks to approximate the softmax layer outputs of
pre-trained teacher neural networks, the proposed self distillation framework
distills knowledge within network itself. The networks are firstly divided into
several sections. Then the knowledge in the deeper portion of the networks is
squeezed into the shallow ones. Experiments further prove the generalization of
the proposed self distillation framework: enhancement of accuracy at average
level is 2.65%, varying from 0.61% in ResNeXt as minimum to 4.07% in VGG19 as
maximum. In addition, it can also provide flexibility of depth-wise scalable
inference on resource-limited edge devices.Our codes will be released on github
soon.Comment: 10page
Big data and the SP theory of intelligence
This article is about how the "SP theory of intelligence" and its realisation
in the "SP machine" may, with advantage, be applied to the management and
analysis of big data. The SP system -- introduced in the article and fully
described elsewhere -- may help to overcome the problem of variety in big data:
it has potential as "a universal framework for the representation and
processing of diverse kinds of knowledge" (UFK), helping to reduce the
diversity of formalisms and formats for knowledge and the different ways in
which they are processed. It has strengths in the unsupervised learning or
discovery of structure in data, in pattern recognition, in the parsing and
production of natural language, in several kinds of reasoning, and more. It
lends itself to the analysis of streaming data, helping to overcome the problem
of velocity in big data. Central in the workings of the system is lossless
compression of information: making big data smaller and reducing problems of
storage and management. There is potential for substantial economies in the
transmission of data, for big cuts in the use of energy in computing, for
faster processing, and for smaller and lighter computers. The system provides a
handle on the problem of veracity in big data, with potential to assist in the
management of errors and uncertainties in data. It lends itself to the
visualisation of knowledge structures and inferential processes. A
high-parallel, open-source version of the SP machine would provide a means for
researchers everywhere to explore what can be done with the system and to
create new versions of it.Comment: Accepted for publication in IEEE Acces
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