Diagnosing cancer involves pathologists reviewing tissue samples. Still, variability among pathologists can complicate the process and result in misdiagnoses. Google is hoping to use deep learning to develop an algorithm capable of detecting cancer.

To address such variability, Google announced on its Google Research Blog a new algorithm to detect breast cancer as well as or even better than a pathologist.

The automated detection algorithm was trained using images of metastasized breast cancer that spread to lymph nodes. The algorithm, which included customization to examine images at varying magnifications, achieved a localization score of 89 percent. This score was significantly higher than the 73 percent by a pathologist with no time limit.

“To ensure the best clinical outcome for patients, these algorithms need to be incorporated in a way that complements the pathologist’s workflow,” according to the blog post by Martin Stumpe, technical lead, and Lily Peng, product manager. “We envision that algorithm such as ours could improve the efficiency and consistency of pathologists.

"For example, pathologists could reduce their false negative rates by reviewing the top-ranked predicted tumor regions including up to eight false positive regions per slide. Training models are just the first of many steps in translating interesting research to a real product. From clinical validation to regulatory approval, much of the journey from 'bench to bedside' still lies ahead—but we are off to a very promising start, and we hope by sharing our work, we will be able to accelerate progress in this space.”

The team published a full paper that can be found here.