Fast and scalable search of whole-slide images via self-supervised deep learning.

ABSTRACT: The adoption of digital pathology has enabled the curation of large repositories of gigapixel whole-slide images (WSIs). Computationally identifying WSIs with similar morphologic features within large repositories without requiring supervised training can have significant applications. However, the retrieval speeds of algorithms for searching similar WSIs often scale with the repository size, which limits their clinical and research potential. Here we show that self-supervised deep learning can be leveraged to search for and retrieve WSIs at speeds that are independent of repository size. The algorithm, which we named SISH (for self-supervised image search for histology) and provide as an open-source package, requires only slide-level annotations for training, encodes WSIs into meaningful

SUBMITTER: Chen C 

PROVIDER: S-EPMC9792371 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature