Identification and Retrieval of Prostate Cancer Cases Using a Content Based Search Tool.

In recent years, large amounts of digital histopathology images have become available. Such images can be useful for pathologists, however, searching for specific cases and similarities within them is not straightforward. In this work, we present a content-based retrieval system and a scale detection method that can allow browsing in heterogeneous prostate histopathology datasets. The system is based on state-of-theart deep convolutional learning networks[1] and handcrafted features. The system allows to retrieve regions of prostate images that are visually similar to manually delineated regions of interest at specific magnification levels. Several image features were tested and compared, showing that a properly tuned retrieval system can enhance the practice of pathologists. [ABSTRACT FROM AUTHOR]