IEEE Spectrum May, 2017 - 45

remove a wart, with either heat or cold. This
fast and relatively painless treatment stops cancer from developing-for a total cost of less than
US $20. (In wealthier countries, women may
opt for long-term monitoring of precancerous
lesions or a more thorough surgery, but these
options aren't usually feasible in places like
rural Kenya.)
Clinics in many developing countries are
adopting visual screening programs, which
have probably saved hundreds of thousands
of lives already. However, the procedure isn't
perfect. Frontline health workers need training and supervision to differentiate between
lesions that are truly signs of impending cancer and the many suspicious-looking conditions that are actually benign. Screeners may
also miss evidence of advanced cancer that
requires referral to a specialist.

making the Call: kenyan nurses practice using
the eva Scope to differentiate between benign
lesions and signs of cervical cancer.

The team at Global Good saw this situation
not as a medical challenge but as a software
engineering challenge. Where human eyes
needed help, we would bring computer vision
backed up by artificial intelligence.

emily Johnson

e started by reviewing images of
the cervix obtained by colposcopes.
We quickly realized that typical
computer vision software couldn't handle
this large and complex data set because the
images had too many features with too much
variability. We simply couldn't design algorithms with detailed and exhaustive procedures for distinguishing between a healthy
cervix and one with signs of trouble.
The situation called for machine learning-
the branch of computer science in which the
computer is given an objective, a software

framework, and a large training data set, and is then left to create its
own solution for carrying out the task at hand.
A common type of machine learning relies on deep neural networks
(DNNs), so named because the computing scheme loosely mimics the
brain's interconnected neurons. Each computing node can be thought
of as a neuron with many inputs. The artificial neuron performs some
function based on those inputs and then outputs a single signal, which
can serve as one of the inputs for other neurons. By arranging many layers of connected neurons, computer scientists enable these networks to
handle tremendously complex tasks.
While the architecture of neural networks is inspired by the human brain,
this brand of AI is far removed from human ways of thought. If somebody
is explaining how to visually identify a bottle of beer in a store that also
stocks bottles of wine, juice, and water, that person would likely describe
its distinguishing features in terms of height, diameter, shape, texture,
color, and patterns. Some descriptors might include analogies such as
"satin finish" or "orange-peel texture." Every feature would be based on,
and limited by, our human senses and perception. Yet that list wouldn't
include all the factors that our brains use to distinguish one object from
another, because much of the process is subconscious.
If we used a human list of features as the basis of an algorithm for recognizing beer bottles, we'd likely get poor results. So instead we'd feed a
DNN thousands and thousands of highly variable images of bottles, with
metadata indicating whether each image does in fact show a beer bottle.
Through a complicated series of training runs, the network can eventually determine, on its own, the relevant distinguishing features. Many
similar experiments have shown that neural networks can identify features quite unlike those any person would come up with. And their lists
of salient features, cryptic as they are, often enable superb performance.
In tasks involving image processing and pattern recognition, the subtype of DNNs previously mentioned called convolutional neural networks
(CNNs) are proving the most promising. This approach uses a few clever
tricks to reduce the heavy computational task of making sense of an image.
Computer scientist Yann LeCun created some of the first CNNs in the
1980s at AT&T Bell Laboratories, using them in computer vision systems that could recognize handwriting, such as the zip codes on envelopes. (Today, LeCun is the director of AI research at Facebook.) But the
potential of CNNs wasn't really explored until 2012, when University of
Toronto graduate student Alex Krizhevsky and his colleagues used a CNN
to win an image recognition challenge that involved 1.4 million photos
of objects in 1,000 different categories. Their AlexNet program had an
error rate more than 50 percent lower than previous winners. It handily
recognized barometers, barbershops, bubbles, baseball players, bullet
trains, bolo ties, burritos, bath towels, and Boston terriers, to name just
a few of the categories under the letter B. (Krizhevsky and several of his
teammates now work at Google.)
Since that game-changing demonstration, CNNs have taken off. Enabled
by the availability of relatively cheap high-performance computing, which
is needed for training these networks, CNNs have been adopted for many
applications involving images.
In the medical field, the possibilities are exciting. For example, Jürgen
Schmidhuber of the Swiss AI Lab IDSIA recently took images of breast cancer cells from real pathology reports and used a CNN to identify dividing
cells that indicate an aggressive tumor-a detection task that's challenging even for trained experts. His demonstration was a proof of concept,
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Table of Contents for the Digital Edition of IEEE Spectrum May, 2017