ANDREI FATEEV: Hello, everyone.
I'm Andrei Fateev, data ML specialist at Google Cloud.
And I'm excited to be joined by Derrick James, director
of AI and ML at Slalom, and Mike Quick, VP of research
and development at Hologic.
We're here to discuss an AI solution we developed
in partnership with Hologic to further their mission
to provide accessible health care for women across the globe
and set the bar for innovation in their industry.
Specifically, we will focus on how we work together
to develop a solution, and we'll walk you
through the steps that turned AI models into reality
with a real business impact, ultimately,
making cervical cancer screening faster,
more efficient, and more readily available around the world.
So let's get started.
Mike, thanks for joining us today.
Could you provide a brief background of Hologic need
for advanced technology in AI to reach your goals?
MIKE QUICK: Sure.
Thanks, Andre.
So at Hologic, we've been involved
in cervical cancer screening and new technologies
for the past 30 years.
And we're really proud of the work
that we've done in partnering together
with clinical laboratories and physicians really
being able to have an impact in the lives of women
around the globe.
The technologies that we've developed
have been able to reduce cervical cancer in many places,
both in the US, in Europe, and in other parts of developing
countries.
However, there's still more work for us to do,
and we're excited to be able to join in the goal of eradicating
cervical cancer in the future.
One of the key areas that we need to address,
though, is the fact that there are not
enough cytotechnologists or pathologists,
the medical professionals that diagnose cervical cancer.
And that's really where we're looking
to artificial intelligence as a way to be able to leverage
that technology to be able to bring
new solutions and new technologies
to have an impact on cervical cancer around the world.
ANDREI FATEEV: This seems like a tough multifaceted problem
to solve, Mike.
On one hand, it's deeply specialized medical diagnostics
dealing with human lives.
And on another hand, it's a sheer technology scale
and cutting edge deep learning techniques requiring
processing of image files.
MIKE QUICK: That's absolutely right, Andre.
In fact, when we look the data that we
have to analyze for solving this problem, it's a real challenge.
The amount of data that's created just
from a single patient can be on the order
of magnitude of 100 gigabytes for just a single slide.
And the reason behind that is we're
actually doing volumetric scanning.
Instead of just looking at a single slide that
contains cellular material, we're
looking at the full volume and scanning multiple planes
of images in order to be able to correctly identify
cervical cancer in its precursor lesions.
So there's a real challenge from the technology standpoint.
In addition on the health care side,
we're really seeing that cervical cancer
is a disease that progresses and regresses over time.
Patients may have precursor lesions
that their own immune system is able to fight that off.
And so it makes it really difficult
to be able to determine what is truth.
And Derrick will be able to share a little bit more
about that and how we address that problem.
ANDREI FATEEV: Thanks for the background, Mike.
Now to Derrick.
As part of the Slalom Solution Team,
can you walk us through some of the challenges you
had to work on and techniques used
to turn this idea into reality?
DERRICK JAMES: Happy to.
This was a rare opportunity for us.
And we were very excited on our side
because it's pretty rare that you
get to use these techniques in machine learning that
have developed over time to really make
a difference in people's lives.
So we were happy to dig into it.
It came down to really two sets of challenges
that we're facing that Mike laid out.
The first was around the methodology itself.
So when you've got labelers of data that are inconsistent
or sometimes incorrect with a disease that
can change over time, how do you define truth?
If a digital psychologist finds that there
is HSIL present in an image but three months later the lady has
fought it off, does that mean he's incorrect
or that they were incorrect?
Obviously not.
So as you remember, Mike, we spent a lot of time
thinking about and defining what is truth and how
do we deal with the fact that we have
all of these different labelers that
are sometimes in disagreement with each other.
Because at the end of the day, what
we need to teach the machine to do is replicate the behavior
that we want to be automated.
And we got to make sure that's the right behavior.
So it took a lot of really deep conversations
with the client, Mike and his team,
to understand what's the right way to approach it.
The second, which Mike alluded to,
was the technical challenge.
As Mike stated, these are high-resolution images,
100,000 pixel by 100,000 pixel.
The underlying structure of the data that we get
is actually what's called a TIFF file.
So this is a highly specialized image file,
which is not actually one image but the several hundred JPEGs
all compressed into a file together.
So you're looking at a file that's somewhere between 500
megabytes and 5 gigabytes uncompressed,
which means that when you--
compressed, so that when you uncompress it,
you're actually dealing with 20 gigabytes plus at table stakes.
Now if you think about this 20-gigabyte image that needs
to be stitched together in a world where we're going
to provide--
along with Hologic and Google-- this valuable service
for women, the question that comes up
is how do we engineer a system that
takes this image that's created from the lab,
decompresses it quickly, accurately makes
the prediction, and then returns it back within seconds
so that we have a good user experience involved.
That involved some pretty heavy and significant computer
engineering.
15, 20 years from now if somebody
is trying to do something like this,
they'll look at the concept of a 20-gig image being
thrown around, and they'll say, what's the big deal?
But here in 2022, there's not many GPUs that can handle it.
There's not many GPUs where you can put that entire array
into memory as you would need to for your traditional neural
network techniques.
So that forced us to have to get pretty creative in how
we broke up this work, distributed it, and stitched it
back together.
That was part of what Google's actually really helpful
with is giving us the flexibility and the power
that we needed in order to accomplish that.
What we ended up doing along with that computer engineering
was to apply a technique that was actually being developed
in natural language processing.
And we used that for this computer vision problem.
So we ended up deploying a technique involved here
that was about eight months early before it was even
submitted at the NIPT conference, which
is the conference on neural information processing.
The result was pretty good sensitivity and pretty good
accuracy all around, which led us to believe that there's
a real possibility of helping to save
women's lives in the future.
ANDREI FATEEV: Thank you, Derrick.
And thank you for joining this session.
We are just getting started on our work together,
and we can't wait to see where this goes next.
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