19,161 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
Molecular self-organisation in a developmental model for the evolution of large-scale artificial neural networks
We argue that molecular self-organisation during embryonic development allows evolution to perform highly nonlinear combinatorial optimisation. A structured approach to architectural optimisation of large-scale Artificial Neural Networks using this principle is presented. We also present simulation results demonstrating the evolution of an edge detecting retina using the proposed methodology
A Neural Model for Generating Natural Language Summaries of Program Subroutines
Source code summarization -- creating natural language descriptions of source
code behavior -- is a rapidly-growing research topic with applications to
automatic documentation generation, program comprehension, and software
maintenance. Traditional techniques relied on heuristics and templates built
manually by human experts. Recently, data-driven approaches based on neural
machine translation have largely overtaken template-based systems. But nearly
all of these techniques rely almost entirely on programs having good internal
documentation; without clear identifier names, the models fail to create good
summaries. In this paper, we present a neural model that combines words from
code with code structure from an AST. Unlike previous approaches, our model
processes each data source as a separate input, which allows the model to learn
code structure independent of the text in code. This process helps our approach
provide coherent summaries in many cases even when zero internal documentation
is provided. We evaluate our technique with a dataset we created from 2.1m Java
methods. We find improvement over two baseline techniques from SE literature
and one from NLP literature
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
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