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The increasing availability of large institutional and public histopathologyimage datasets is enabling the searching of these datasets for diagnosis,research, and education. Though these datasets typically have associatedmetadata such as diagnosis or clinical notes, even carefully curated datasetsrarely contain annotations of the location of regions of interest on eachimage. Because pathology images are extremely large (up to 100,000 pixels ineach dimension), further laborious visual search of each image may be needed tofind the feature of interest. In this paper, we introduce a deep learning basedreverse image search tool for histopathology images: Similar Medical ImagesLike Yours (SMILY). We assessed SMILY's ability to retrieve search results intwo ways: using pathologist-provided annotations, and via prospective studieswhere pathologists evaluated the quality of SMILY search results. As a negativecontrol in the second evaluation, pathologists were blinded to whether searchresults 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, toimprove the efficiency of searching large archives of histopathology images,without the need to develop and implement specific tools for each application.

Similar Image Search for Histopathology: SMILY