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- W2396560196 abstract "A combination of several classifiers using global features for the content description of medical images is proposed. Beside two texture features, downscaled representations of the original images are used, which preserve spatial information and utilize distance measures which are robust regarding common variations in radiation dose, translation, and local deformation. No query refinement mechanisms are used. The single classifiers are used within a parallel combination scheme, with the optimization set being used to obtain the best weighing parameters. For the medical automatic annotation task, a categorization rate of 78.6% is obtained, which ranks 12th among 28 submissions. When applied in the medical retrieval task, this combination of classifiers yields a mean average precision (MAP) of 0.0172, which is rank 11 of 11 submitted runs for automatic, visual only systems." @default.
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- W2396560196 date "2006-01-01" @default.
- W2396560196 modified "2023-09-24" @default.
- W2396560196 title "Combining Global Features within a Nearest Neighbor Classifier for Content-based Retrieval of Medical Images." @default.
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