This paper proposes a model for content-based retrieval of histopathology images. The most remarkable characteristic of the proposed model is that it is able to extract high-level features that reflect the semantic content of the images. This is accomplished by a semantic mapper that maps conventional low-level features to high-level features using state-of-the-art machine-learning techniques. The semantic mapper is trained using images labeled by a pathologist. The system was tested on a collection of 1502 histopathology images and the performance assessed using standard measures. The results show an improvement from a 67% of average precision for the first result, using low-level features, to 80% of precision using high-level features. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Caicedo, J. C., Gonzalez, F. A., & Romero, E. (2008). A semantic content-based retrieval method for histopathology images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4993 LNCS, pp. 51–60). https://doi.org/10.1007/978-3-540-68636-1_6
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