The science of deep learning, a sub-discipline of artificial intelligence (AI), is only a recent development in the grand scheme of things, but during its short existence, it has been producing some impressive technological achievements. Advances in image recognition, language understanding, and translation have led to the development of virtual assistants, smart home speakers, and gains in cybersecurity, and they are leading the charge toward autonomous driving. Now, companies have found a way to use those AI smarts to fight cancer.Deep learning involves the construction of artificial neural networks, using software and complex algorithms to recreate the capacity of the human brain to learn. These learning computers have a particular knack for sifting through vast amounts of data and recognizing patterns, getting smarter as they go. The first breakthrough involved feeding a system thousands of pictures of cats until the program was able to recognize a cat on its own.

GoogLeNet AI provides groundbreaking cancer research! Image source: Getty Images. Alternative text: Radiology technician reviewing mammography results.

GoogLeNet AI provides groundbreaking cancer research! Image source: Getty Images.

Recognizing breast cancer tumors

This ability to identify patterns has led to a significant breakthrough in the area of breast cancer research. Last month, in a paper titled Detecting Cancer Metastases on Gigapixel Pathology ImagesAlphabet Inc. (NASDAQ:GOOG) (NASDAQ:GOOGL) division Google announced that it had created a neural network that could analyze medical images and identify tumors with a greater degree of accuracy than human pathologists. The study revealed that the company, using its GoogLeNet AI, reviewed thousands of medical images supplied by a Dutch university and was able to identify malignant tumors in breast cancer images with an 89% accuracy rate, compared to 73% for its human counterparts. In a blog, Google researchers explained:

Pathologists are responsible for reviewing all the biological tissues visible on a slide. However, there can be many slides per patient, each of which is 10+ gigapixels when digitized at 40X magnification. Imagine having to go through a thousand 10 megapixel (MP) photos, and having to be responsible for every pixel. Needless to say, this is a lot of data to cover, and often time is limited.

IBM enhances Watson’s ability to “see” medical images. Image source: IBM. Alternative text: Woman reviewing X-Rays on lighted panel.

This technology has the potential to provide initial screenings, allowing doctors to review only those images that have been flagged as potentially cancerous. The system still requires improvement, as it generated a number of false positives — identifying cancerous cells where none were present. So, while AI won’t be replacing pathologists anytime soon, these algorithms could be used to pre-screen images and not only reduce the workload on doctors, but also serve parts of the world where pathologists are in short supply.


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