Study finds Google system could improve breast cancer detection

CHICAGO — A Google artificial intelligence system proved as good as expert radiologists at detecting which women had breast cancer based on screening mammograms and showed promise at reducing errors, researchers in the United States and Britain reported.

The study, published in the journal Nature on Wednesday, is the latest to show that artificial intelligence (AI) has the potential to improve the accuracy of screening for breast cancer, which affects one in eight women globally.

Radiologists miss about 20 percent of breast cancers in mammograms, the American Cancer Society says, and half of all women who get the screenings over a 10-year period have a false positive result.

The findings of the study, developed with Alphabet Inc's DeepMind AI unit, which merged with Google Health in September, represent a major advance in the potential for the early detection of breast cancer, Mozziyar Etemadi, one of its co-authors from Northwestern Medicine in Chicago, said.

The team, which included researchers at Imperial College London and Britain's National Health Service, trained the system to identify breast cancers on tens of thousands of mammograms.

They then compared the system's performance with the actual results from a set of 25,856 mammograms in the United Kingdom and 3,097 from the United States.

The study showed the AI system could identify cancers with a similar degree of accuracy to expert radiologists, while reducing the number of false positive results by 5.7 percent in the US-based group and by 1.2 percent in the British-based group. 

It also cut the number of false negatives, where tests are wrongly classified as normal, by 9.4 percent in the US group, and by 2.7 percent in the British group.

These differences reflect the ways in which mammograms are read. In the United States, only one radiologist reads the results and the tests are done every one to 2 years. In Britain, the tests are done every 3 years, and each is read by 2 radiologists. When they disagree, a third is consulted.

'SUBTLE CUES'

In a separate test, the group pitted the AI system against 6 radiologists and found it outperformed them at accurately detecting breast cancers.

Connie Lehman, chief of the breast imaging department at Harvard's Massachusetts General Hospital, said the results are in line with findings from several groups using AI to improve cancer detection in mammograms, including her own work.

The notion of using computers to improve cancer diagnostics is decades old, and computer-aided detection (CAD) systems are commonplace in mammography clinics, yet CAD programs have not improved performance in clinical practice.

The issue, Lehman said, is that current CAD programs were trained to identify things human radiologists can see, whereas with AI, computers learn to spot cancers based on the actual results of thousands of mammograms.

This has the potential to "exceed human capacity to identify subtle cues that the human eye and brain aren't able to perceive," Lehman added.

Although computers have not been "super helpful" so far, "what we've shown at least in tens of thousands of mammograms is the tool can actually make a very well-informed decision," Etemadi said.

The study has some limitations. Most of the tests were done using the same type of imaging equipment, and the US group contained a lot of patients with confirmed breast cancers.

Crucially, the team has yet to show the tool improves patient care, said Dr Lisa Watanabe, chief medical officer of CureMetrix, whose AI mammogram program won US approval last year.

"AI software is only helpful if it actually moves the dial for the radiologist," she said.

Etemadi agreed that those studies are needed, as is regulatory approval, a process that could take several years. 

source: news.abs-cbn.com

Rise of the Machines: video gamers beware

PARIS, France - Researchers unveiled a software system Wednesday which had taught itself to play 49 different video games and proceeded to defeat human professionals -- a major step in the fast-developing Artificial Intelligence realm.

Not only did the system give flesh-and-blood gamers a run for their money, it discovered tricks its own programmers didn't even know existed, a team from Google-owned research company DeepMind reported in the scientific journal Nature.




 "This... is the first time that anyone has built a single general learning system that can learn directly from experience to master a wide range of challenging tasks," said co-developer Demis Hassabis.

The feat brings us closer to a future run by smart, general-purpose robots which can teach themselves to perform a task, store a "memory" of trial and error and adapt their actions for a better outcome next time.

Such machines may be able to do anything from driving our cars to planning our holidays and conduct scientific research, said the team.

Inspired by the human learning process, the "artificial agent" dubbed deep Q-network (DQN) was let loose, with only minimal programming, on an Atari game console from the 1980s.

"The only information they (the system) get is the pixels (on the screen) and the game score and the goal they've been told is to maximise the score," Hassabis explains in a Nature video.

"Apart from that, they have no idea about what kind of game they are playing, what their controls do, what they're even controlling in the game."

Unlike humans, the algorithm-based software starts off without the benefit of previous experience.

Presented with an on-screen paddle and ball, for example, a human gamer would already know that the goal must somehow involve striking the one with the other.

The system, by comparison, learns by activating computer keys randomly until it starts scoring through trial and error.

Learns, adapts, and gets better

"The system kind of learns, adapts and gets better and better incrementally until eventually it becomes almost perfect on some of the games," said Hassabis.

Games included the late 1970's classic Breakout, in which the player has to break through several layers of bricks at the top of a computer screen with a "ball" bounced off a paddle sliding from side to side at the bottom, Ms Pac-Man which entails gobbling pellets along a maze, pinball, boxing, tennis and a car race called Enduro.




The system outperformed professional human players in many of the games, but fared poorly in some, including Ms Pac-Man.

In particular game types, explained DeepMind colleague Vlad Mnih, "it's very difficult to get your first points or first rewards, so if the game involves solving a maze then pressing keys randomly will not actually get you any points and then the system has nothing to learn from."

But it did discover aspects of games that its creators hadn't even known about. It figured out, for example, that in Breakout the optimal strategy is to dig a tunnel through one side of the wall and send the ball in to bounce behind it, breaking the bricks from the back.

To the future and beyond

The creators said their system was further advanced in many ways than Watson, an AI question-answering system that outwitted the most successful human players of quiz game Jeopardy in 2011, and Deep Blue, the computer which beat chess master Garry Kasparov in 1997.

Both these had largely been preprogrammed with their particular abilities.

"Whereas what we've done is build algorithms that learn from the ground up, so literally you give them perceptual experience and they learn how to do things directly from that perceptual experience," Hassabis told journalists.

"The advantage of these types of systems is that they can learn and adapt to unexpected things and also... the programmers or the system designers don't necessarily have to know the solution themselves in order for the machine to master that task."

The long-term goal, he added, was to build smart, general-purpose machines.

"We are many decades off from doing that," said the researcher. "But I do think that this is the first significant rung of the ladder that we're on."

The next developmental step will entail tests with 3D video games from the 90s.

source: www.abs-cbnnews.com