Digital Pathology Deep Learning Tool Diagnoses Rare Cancers

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."