From Duke University
4
October 31,,, 2019 -- It can take years
of birdwatching experience to tell one species from the next. But using an
artificial intelligence technique called deep learning, Duke University researchers
have trained a computer to identify up to 200 species of birds from just a
photo.
The real innovation, however, is that
the A.I. tool also shows its thinking, in a way that even someone who doesn't
know a penguin from a puffin can understand.
The team trained their deep neural
network -- algorithms based on the way the brain works -- by feeding it 11,788
photos of 200 bird species to learn from, ranging from swimming ducks to
hovering hummingbirds.
The researchers never told the network
"this is a beak" or "these are wing feathers." Given a
photo of a mystery bird, the network is able to pick out important patterns in
the image and hazard a guess by comparing those patterns to typical species
traits it has seen before.
Along the way it spits out a series of
heat maps that essentially say: "This isn't just any warbler. It's a
hooded warbler, and here are the features -- like its masked head and yellow
belly -- that give it away."
Duke computer science Ph.D. student
Chaofan Chen and undergraduate Oscar Li led the research, along with other team
members of the Prediction Analysis Lab directed by Duke professor Cynthia
Rudin.
They found their neural network is able
to identify the correct species up to 84% of the time -- on par with some of
its best-performing counterparts, which don't reveal how they are able to tell,
say, one sparrow from the next.
Rudin says their project is about more
than naming birds. It's about visualizing what deep neural networks are really
seeing when they look at an image.
Similar technology is used to tag people
on social networking sites, spot suspected criminals in surveillance cameras,
and train self-driving cars to detect things like traffic lights and
pedestrians.
The problem, Rudin says, is that most
deep learning approaches to computer vision are notoriously
opaque. Unlike
traditional software, deep learning software learns from the data without being
explicitly programmed. As a result, exactly how these algorithms 'think' when
they classify an image isn't always clear.
Rudin and her colleagues are trying to
show that A.I. doesn't have to be that way. She and her lab are designing deep
learning models that explain the reasoning behind their predictions, making it
clear exactly why and how they came up with their answers. When such a model
makes a mistake, its built-in transparency makes it possible to see why.
For their next project, Rudin and her
team are using their algorithm to classify suspicious areas in medical images
like mammograms. If it works, their system won't just help doctors detect
lumps, calcifications and other symptoms that could be signs of breast cancer.
It will also show which parts of the mammogram it's homing in on, revealing
which specific features most resemble the cancerous lesions it has seen before
in other patients.
In that way, Rudin says, their network
is designed to mimic the way doctors make a diagnosis. "It's case-based
reasoning," Rudin said. "We're hoping we can better explain to
physicians or patients why their image was classified by the network as either
malignant or benign."
The team is presenting a paper on their
findings at the Thirty-third Conference on Neural Information Processing
Systems (NeurIPS 2019) in Vancouver on December 12.
Other authors of this study include
Daniel Tao and Alina Barnett of Duke and Jonathan Su at MIT Lincoln Laboratory.
CITATION: "This Looks Like That:
Deep Learning for Interpretable Image Recognition," Chaofan Chen, Oscar
Li, Daniel Tao, Alina Barnett, Jonathan Su and Cynthia Rudin. Electronic
Proceedings of the Neural Information Processing Systems Conference. December
12, 2019.
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