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Learning to interpret medical images
Proceedings IEEE International Symposium on Biomedical Imaging, 2003Statistical shape and appearance models are proving valuable tools for medical image analysis. In this paper we outline the methodology and highlight important recent developments. In particular, we consider an extension to images of differing modality, and describe an optimisation approach to establishing correspondences across a training set of ...
Christopher J. Taylor 0001 +3 more
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Interpretation of Computed Tomographic Images
Veterinary Clinics of North America: Small Animal Practice, 1993This article discusses the production of optimal CT images in small animal patients as well as principles of radiographic interpretation. Technical factors affecting image quality and aiding image interpretation are included. Specific considerations for scanning various anatomic areas are given, including indications and potential pitfalls.
R L, Stickle, J T, Hathcock
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Qualitative probabilities for image interpretation
Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999Two basic problems in image interpretation are: a) determining which interpretations are the most plausible amongst many possibilities; and b) controlling the search for plausible interpretations. We address these issues using a Bayesian approach, with the plausibility ordering and search pruning based on the posterior probabilities of interpretations.
Allan D. Jepson, Richard Mann
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Medical images and automated interpretation
Journal of Biomedical Engineering, 1988The application of automated interpretation to medical images is discussed and the main methods of medical imaging are briefly described. The factors behind the process of human clinical interpretation are also considered. Because human interpretation can be aided by processing of the raw image, the standard methods of image processing are mentioned ...
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Learning paradigms for image interpretation
Spatial Vision, 2000In this paper we discuss image understanding and object recognition as a class of processes which involve binding what is seen with what is known. It follows from this perspective that it is important to explicate how systems may learn about spatial information from images, how it is encoded, and, how, in turn, such knowledge is matched with new image ...
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Interpreting visual images, individually
Trends in Cognitive Sciences, 2002Visual images seem picture-like, but is this epiphenomenal or does it reflect a functional aspect of how they are represented in the brain? Visualising ambiguous pictures – those with more than one interpretation – provides one method for examining this issue.
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Learning Interpretable Representations of Images
2019Computers represent images with pixels and each pixel contains three numbers for red, green and blue colour values. These numbers are meaningless for humans and they are mostly useless when used directly with classical machine learning techniques like linear classifiers.
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Interpretation of generalized grating imaging
Journal of the Optical Society of America A, 2008This paper describes generalized grating imaging with equations based on geometrical optics and wave optics. It shows that the equation derived by wave optics agrees well with the description by geometrical optics. The derived equation based on wave optics can be used to deduce and calculate the intensity distribution of the grating image.
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On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021Xiao Xiang Zhu +2 more
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