Rare diseases are often difficult to diagnose, and predicting the best course of treatment can be challenging for clinicians. To help address these challenges, investigators from the Mahmood Lab at Harvard Medical School and Brigham and Women’s Hospital have developed a deep- learning algorithm that can teach itself to learn features that can then be used to find similar cases in large pathology image repositories.
Known as SISH (self-supervised image search for histology), the new tool acts like a search engine for pathology images and has many potential applications, including identifying rare diseases and helping clinicians determine which patients are likely to respond to similar therapies. A paper describing the self-teaching algorithm is published in Nature Biomedical Engineering on Oct. 10.
“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, assistant professor of pathology at HMS at Brigham and Women’s. “This system has the potential to improve pathology training, disease subtyping, tumor identification, and rare morphology identification.”