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.
@article{arxiv.2401.03271,
title = {Analysis and Validation of Image Search Engines in Histopathology},
author = {Isaiah Lahr and Saghir Alfasly and Peyman Nejat and Jibran Khan and Luke Kottom and Vaishnavi Kumbhar and Areej Alsaafin and Abubakr Shafique and Sobhan Hemati and Ghazal Alabtah and Nneka Comfere and Dennis Murphee and Aaron Mangold and Saba Yasir and Chady Meroueh and Lisa Boardman and Vijay H. Shah and Joaquin J. Garcia and H. R. Tizhoosh},
journal= {arXiv preprint arXiv:2401.03271},
year = {2024}
}