Chen et al. [Chen2022] recently published the article "Fast and scalable search of whole-slide images via self-supervised deep learning" in Nature Biomedical Engineering. The authors call their method "self-supervised image search for histology", short SISH. The paper is not easily readable, and many important details are buried under ambiguous descriptions. Incremental modification of Yottixel - Yottixel introduced the concept of "mosaic" through a customized clustering and selection process [Kalra2020a]. While Chen et al. frequently mention "Yottixel" and "mosaic," they only acknowledge once that they have followed the Yottixel's mosaic generation process.

Researchers in the Mahmood lab at Brigham and Women's Hospital have developed a new deep learning algorithm that is capable of teaching itself to search large datasets of pathology images to identify similar cancer cases. The tool, called SISH for "Self-Supervised Image Search for Histology," has the ability to identify analogous features in pathology images and uses that information to both pinpoint the form of disease, while also helping doctors and other clinicians determine which therapies will be most effective for each patient. Details of the algorithm were published today in the journal Nature Biomedical Engineering. "We show that our system can assist with the diagnosis of rare diseases and find cases with similar morphologic patterns without the need for manual annotations, and large datasets for supervised training," said senior author Faisal Mahmood, PhD, in the Brigham's Department of Pathology. "This system has the potential to improve pathology training, disease subtyping, tumor identification, and rare morphology identification."

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 discrete latent representations and leverages a tree data structure for fast searching followed by an uncertainty-based ranking algorithm for WSI retrieval. We evaluated SISH on multiple tasks (including retrieval tasks based on tissue-patch queries) and on datasets spanning over 22,000 patient cases and 56 disease subtypes. SISH can also be used to aid the diagnosis of rare cancer types for which the number of available WSIs is often insufficient to train supervised deep-learning models. A self-supervised deep-learning algorithm searches for and retrieves gigapixel whole-slide images at speeds that are independent of the size of the image repository