Abstract
The detection of specific features in medical images is often a key support for diagnosis. Taking advantage of large bases of images where features of interest have been localized by clinicians, a modular system has been developed to spot similar features on new images. Images are scanned through a window, the size of which being previously fitted to the feature of interest. The recognition process involves a coding phase followed by a classification phase. These phases rely on unsupervised and supervised learning respectively for their implementation. Applications in Dermatology and Ophthalmology are presented.
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© 2000 Springer-Verlag London
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Serruys, C. et al. (2000). A Learning by Sample Approach for the Detection of Features in Medical Images.. In: Malmgren, H., Borga, M., Niklasson, L. (eds) Artificial Neural Networks in Medicine and Biology. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0513-8_14
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DOI: https://doi.org/10.1007/978-1-4471-0513-8_14
Publisher Name: Springer, London
Print ISBN: 978-1-85233-289-1
Online ISBN: 978-1-4471-0513-8
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