23,157 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
Numerical methods for multiscale inverse problems
We consider the inverse problem of determining the highly oscillatory
coefficient in partial differential equations of the form
from given
measurements of the solutions. Here, indicates the smallest
characteristic wavelength in the problem (). In addition to the
general difficulty of finding an inverse, the oscillatory nature of the forward
problem creates an additional challenge of multiscale modeling, which is hard
even for forward computations. The inverse problem in its full generality is
typically ill-posed and one common approach is to replace the original problem
with an effective parameter estimation problem. We will here include microscale
features directly in the inverse problem and avoid ill-posedness by assuming
that the microscale can be accurately represented by a low-dimensional
parametrization. The basis for our inversion will be a coupling of the
parametrization to analytic homogenization or a coupling to efficient
multiscale numerical methods when analytic homogenization is not available. We
will analyze the reduced problem, , by proving uniqueness of the inverse
in certain problem classes and by numerical examples and also include numerical
model examples for medical imaging, , and exploration seismology,
Inviwo -- A Visualization System with Usage Abstraction Levels
The complexity of today's visualization applications demands specific
visualization systems tailored for the development of these applications.
Frequently, such systems utilize levels of abstraction to improve the
application development process, for instance by providing a data flow network
editor. Unfortunately, these abstractions result in several issues, which need
to be circumvented through an abstraction-centered system design. Often, a high
level of abstraction hides low level details, which makes it difficult to
directly access the underlying computing platform, which would be important to
achieve an optimal performance. Therefore, we propose a layer structure
developed for modern and sustainable visualization systems allowing developers
to interact with all contained abstraction levels. We refer to this interaction
capabilities as usage abstraction levels, since we target application
developers with various levels of experience. We formulate the requirements for
such a system, derive the desired architecture, and present how the concepts
have been exemplary realized within the Inviwo visualization system.
Furthermore, we address several specific challenges that arise during the
realization of such a layered architecture, such as communication between
different computing platforms, performance centered encapsulation, as well as
layer-independent development by supporting cross layer documentation and
debugging capabilities
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