Analysis and Validation of Image Search Engines in Histopathology
Isaiah Lahr Saghir Alfasly Peyman Nejat Jibran Khan Luke Kottom Vaishnavi Kumbhar Areej Alsaafin Abubakr Shafique Sobhan Hemati Ghazal Alabtah Nneka Comfere Dennis Murphee Aaron Mangold Saba Yasir Chady Meroueh Lisa Boardman Vijay H. Shah Joaquin J. Garcia H. R. Tizhoosh
Searching for similar images in archives of histology and histopathology
images is a crucial task that may aid in patient matching for various purposes,
ranging from triaging and diagnosis to prognosis and prediction. Whole slide
images (WSIs) are highly detailed digital representations of tissue specimens
mounted on glass slides. Matching WSI to WSI can serve as the critical method
for patient matching. In this paper, we report extensive analysis and
validation of four search methods bag of visual words (BoVW), Yottixel, SISH,
RetCCL, and some of their potential variants. We analyze their algorithms and
structures and assess their performance. For this evaluation, we utilized four
internal datasets ($1269$ patients) and three public datasets ($1207$
patients), totaling more than $200,000$ patches from $38$ different
classes/subtypes across five primary sites. Certain search engines, for
example, BoVW, exhibit notable efficiency and speed but suffer from low
accuracy. Conversely, search engines like Yottixel demonstrate efficiency and
speed, providing moderately accurate results. Recent proposals, including SISH,
display inefficiency and yield inconsistent outcomes, while alternatives like
RetCCL prove inadequate in both accuracy and efficiency. Further research is
imperative to address the dual aspects of accuracy and minimal storage
requirements in histopathological image search.