Google AI Spots Advanced Breast Cancer Better Than Humans

Google AI Spots Advanced Breast Cancer Better Than Humans

This is one of the reasons why almost half a million women die worldwide and are caused by breast cancer, which is estimated to be 90% as the result of metastasis. According to the official blog post by Google, 1 in 4 metastatic lymph nodes stage assessments (which judge how many lymph nodes have been affected by cancer) would be revised in retrospect, and only 38% of small metastases were spotted at all by pathologists, who were given limited time to examine various slides.

Last week Google released two research papers, which were published in The American Journal of Surgical Pathology and the Archives of Pathology and Laboratory Medicine.

LYNA wasn't ideal - it occasionally misidentified giant cells, germinal cancers, and bone marrow-derived white blood cells known as histiocytes - but managed to perform better than a pathologist. Further, LYNA was able to accurately pinpoint the location of both cancers and other suspicious regions within each slide, some of which were too small to be consistently detected by pathologists.

Google stressed that it is important for pathologists and the algorithm to work together.

Introduced past year, the LYmph Node Assistant (LYNA) algorithm was trained to recognize characteristics of tumors in metastatic cancer.

LYNA is based on the Inception-v3 model, an open source image recognition model, which has been proven to achieve an accuracy greater than 78.1% or more in Stanford's ImageNet data set.

In a trial of the software, doctors using LYNA were able to carry out inspections of cancer scans in half the time and with better accuracy. It also has yet to be used in real-life clinical situations.

Because of the extremely reliable diagnosis, the AI system enables doctors to do more caring for the patient rather than worrying about the diagnosis stage. Recently, Google's Medical Brain team claimed to have developed an AI system which could predict mortality rates with 90% accuracy.

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