Using AI to detect breast cancer

Roberto Saracco October 16, 2018 Blog 561 Views

Above: Left: a slide containing lymph nodes. Right: LYNA identifying the tumor region.
Image Credit: Google

Image recognition has progressed in ways that were completely unexpected just 20 years ago. At that time we kept saying that computers were no good in understanding images, that was humans turf.

This is no longer so. The camera in our phones recognise a smiling face, the photo management application on our computer can associate faces to person much better than what we can do (I have experienced several times how my iPhoto app can spot an recognise me when I was a little kid -black and white at that time, and grainy- much better than me). It is not that software has grown to emulate (and exceed) our brain, it is just using a completely different approach to image recognition and it works better than our.

As medical radiography, CAT, fMRI has moved digital the amount of digital data has ballooned and this has allowed the training of machines, using various forms of Artificial Intelligence. Automatic systems for analysing medical digital images have grown in the last 2 years becoming more and more effective.

Google has just announced the availability of LYNA -Lymph Node Assistant- based on an Open Source Image recognition software (Inception-v3) using deep learning technologies and have perfected, through training the system achieving a 99% accuracy in detecting malignancy, much better than the best doctors in the field.

I see this as just a first (amazing) step towards tomorrow healthcare where software and robots will play a major role.

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About Roberto Saracco

Roberto Saracco fell in love with technology and its implications long time ago. His background is in math and computer science. Until April 2017 he led the EIT Digital Italian Node and then was head of the Industrial Doctoral School of EIT Digital up to September 2018. Previously, up to December 2011 he was the Director of the Telecom Italia Future Centre in Venice, looking at the interplay of technology evolution, economics and society. At the turn of the century he led a World Bank-Infodev project to stimulate entrepreneurship in Latin America. He is a senior member of IEEE where he leads the New Initiative Committee and co-chairs the Digital Reality Initiative. He is a member of the IEEE in 2050 Ad Hoc Committee. He teaches a Master course on Technology Forecasting and Market impact at the University of Trento. He has published over 100 papers in journals and magazines and 14 books.

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