  Histopathology digital scans are large-size images that contain valuable
information at the pixel level. Content-based comparison of these images is a
challenging task. This study proposes a content-based similarity measure for
high-resolution gigapixel histopathology images. The proposed similarity
measure is an expansion of cosine vector similarity to a matrix. Each image is
divided into same-size patches with a meaningful amount of information (i.e.,
contained enough tissue). The similarity is measured by the extraction of
patch-level deep embeddings of the last pooling layer of a pre-trained deep
model at four different magnification levels, namely, 1x, 2.5x, 5x, and 10x
magnifications. In addition, for faster measurement, embedding reduction is
investigated. Finally, to assess the proposed method, an image search method is
implemented. Results show that the similarity measure represents the slide
labels with a maximum accuracy of 93.18\% for top-5 search at 5x magnification.
