  The increasing availability of large institutional and public histopathology
image datasets is enabling the searching of these datasets for diagnosis,
research, and education. Though these datasets typically have associated
metadata such as diagnosis or clinical notes, even carefully curated datasets
rarely contain annotations of the location of regions of interest on each
image. Because pathology images are extremely large (up to 100,000 pixels in
each dimension), further laborious visual search of each image may be needed to
find the feature of interest. In this paper, we introduce a deep learning based
reverse image search tool for histopathology images: Similar Medical Images
Like Yours (SMILY). We assessed SMILY's ability to retrieve search results in
two ways: using pathologist-provided annotations, and via prospective studies
where pathologists evaluated the quality of SMILY search results. As a negative
control in the second evaluation, pathologists were blinded to whether search
results were retrieved by SMILY or randomly. In both types of assessments,
SMILY was able to retrieve search results with similar histologic features,
organ site, and prostate cancer Gleason grade compared with the original query.
SMILY may be a useful general-purpose tool in the pathologist's arsenal, to
improve the efficiency of searching large archives of histopathology images,
without the need to develop and implement specific tools for each application.
