[MUSIC PLAYING]
SPEAKER 1: It's my pleasure
to introduce my friend Le Lu.
He is a staff scientist
at Environmental Radiology
the Imaging Sciences at NIH.
And he is collaborating
with us [INAUDIBLE]
together to open source a
NIH data set [INAUDIBLE]..
And his research is focused
on medical image understanding
and symantic parsing, so fitting
to new clinical practices,
especially in the areas
of preventive early cancer
detection that announces
and relatively novel
precision imaging biomarkers
with large scale imaging
protocols and statistical
learning principles.
He has worked on various core
R&D problems in chronic--
LE LU: [INAUDIBLE].
SPEAKER 1: And long nodule
[INAUDIBLE] systems.
And he used to work at Siemens
as a senior staff scientist.
He has [INAUDIBLE] 18 US
and international patents
and has served at a
lot of conferences
like [INAUDIBLE]
and [INAUDIBLE]..
And he is hosting the Third
Medical Imaging Workshop
at [INAUDIBLE].
LE LU: Should I stand?
SPEAKER 1: Sure.
It's up to you.
LE LU: OK, I feel this
will be more energy.
So the slides is a a little
bit, like you say, this
is the first version of it,
and about one year ago, I
gave a talk at GTC that
I tried to summarize
what we do at the time.
Medical imaging is booming,
but it's not that much,
but this year,
just after a year,
I really see a
tremendous interest.
Everywhere is just
a lot of things.
So I keep on updating this.
This is kind of a log.
My intention is not to
cover all the [INAUDIBLE]
of the problem we are working
on, why we're working on,
and how is that
indicated clinically.
You know, my job--
a better side of my
job is I'm flexible.
I have really love of freedom
to do the things I like to do.
And I collaborate with really
the best doctors, especially
Ron Summers.
He is one of the
visionary doctors,
and he understands
[INAUDIBLE] very well,
and I think he attended the
[INAUDIBLE] course, while he
was on sabbatical
at Stanford, a lot
of these kind of connections.
And sometimes I
explained things to him.
I feel sometimes he
understands better
than my manager [INAUDIBLE]
even [INAUDIBLE]..
Those doctors are amazing.
They are very smart, and
they have a lot of insight.
I think that helps us.
[INAUDIBLE] enough,
for example, one thing
I am trying to consider is, how
they would train the residents.
How this big hospital--
they can train successful,
like, a highly effective doctor?
What is the curriculum?
How are they doing that?
Did they learn from [INAUDIBLE]
or with [INAUDIBLE]??
Resident indeed
do a [INAUDIBLE]..
So they have a book--
this big book, and they talk
about all the normal variations
that's in a similar disease.
So they were a doctor, you
know, they can resident
for maybe one year or two.
They are starting to
diagnose different diseases.
Then they learned the cost.
[INAUDIBLE]
So they just don't know
what they are doing wrong.
So because they
really look similar--
very, very similar
to the pathology,
but they are normal variations.
So the human have a lot
of normal variations.
The really long-tail problem
is similar to [INAUDIBLE]..
It is really a long-tail issues.
And after half a year
from that, [INAUDIBLE]
actually began to learn
things new and better.
So they improved that permanent.
So this is the-- there
is a lot of things
I think we should be really
cutting into at least all
this big and successful
reputable medical institutions
how they do things in a way.
I think the ways
we're trying to see
is how we can make
their job easier,
how we can make
them more effective,
or they can carry more patients
better, faster in a way.
But we indeed have a
lot of [INAUDIBLE]..
They have hudreds of
years [INAUDIBLE]..
And each hospital,
they will [INAUDIBLE]
like an [INAUDIBLE],,
more than 100 years old.
So we have a lot of things.
We have the [INAUDIBLE].
So I said, the [INAUDIBLE] is
more for the general audience.
I guess from last
year, still comes
people saying whether deep
learning is useful or not.
It's a strong yes.
We did several things.
We did work on all three lines.
And I think we showed
[INAUDIBLE] progress
[INAUDIBLE].
And another thing
I have to mention
is that neural deep
learning is not
necessary to solve all
problems better than before
in radiology, but many, many.
I am trying to say,
let's say, to examine
a heart situation,
the [INAUDIBLE],,
like [INAUDIBLE] modeling
with [INAUDIBLE]..
They can be more
efficient, very fast.
And not necessarily--
you will just be better.
And my focusing,
one, we can do more.
We can do something which cannot
be done before or with poor
performance about
[INAUDIBLE],, yeah,
[INAUDIBLE] problem how
we can solve them better.
So we are focusing on
filling in the gaps.
So some very important problems
they were understudied.
They cannot put into a
product, and we want to put it
into translational things.
When I was [INAUDIBLE],,
we do like [INAUDIBLE]
in a way like a week.
We are very much
product focused.
We do everything
is for the product
release after a half year,
six months, nine months,
we have a deadline to
[INAUDIBLE] product.
So [INAUDIBLE] learn how to do
product at Siemens and fitting
in R&D to [INAUDIBLE].
And we are still
kind of running.
I'm trying to take the best
of both worlds [INAUDIBLE]..
So this is something,
you know, by training,
I'm a [INAUDIBLE] person.
So I think this thing
could last forever.
It's no matter deep
learning or not,
because this is not
just deep learning.
This is really I
feel for my life is
very useful and very powerful
methodologies I will always
kept in mind.
And this really helps to
build a robust product
in my perspective.
I can give a lot
examples [INAUDIBLE]..
It's not saying-- I have to
show a Microsoft to you guys
at a Google place.
I have had these
slides for a long time.
I want to say I
will emphasize this.
So [INAUDIBLE] worked together
as specialist doctors.
Once you really work
with doctors [INAUDIBLE],,
you know how amazing they are.
And our job is really
how to help them,
how to make them
more efficient, how
to do all the dirty
work for them,
and they can do [INAUDIBLE] with
less guessing, more work, more
[INAUDIBLE].
So this is my perspective.
I have been keep--
I am giving what
I'm trying to do,
thinking what's really something
we can do to [INAUDIBLE] yet.
So really it's like you
have at the beginning
think about how build a human
machine collaborative system.
At Siemens, like four
or five years ago,
I didn't know at that time
that wasn't just beginning
of the deep learning
network, not yet.
I think, at that time,
Siemens really built
an amazing product
even software-wise,
but it doesn't sell
well for many reasons.
I think one of the
reasons is really,
one problem is with neural
deep learning, some problems we
don't reach the performance--
performance is a barrier,
like to really introduce
a new business model,
a new protocol,
new clinical particle,
you have to have
the new clinical
protocol that should
be better and revolutionary
and really like it.
I really feel that
[INAUDIBLE] is--
they are not inseparable.
So doctors don't really
know how you make these.
And I ask them, which
one [INAUDIBLE]??
[INAUDIBLE] they
know [INAUDIBLE]
pictures will [INAUDIBLE].
And it is hard for them
to know how much neural
is making the decision.
So the good doctors really
don't need a path per se.
So the good doctor has
[INAUDIBLE] just amazing,
well, you know,
our brains do work.
We do a lot of high-level
tasks much better
than a machine does now.
The machine has a lot of--
I think we have a lot
of problems to solve,
a lot of challenges to solve.
You know, when put machines
into a complicated task,
they are just miserable.
And instead, it is [INAUDIBLE],,
but it is still miserable.
Of course, they are going to do
certain things better, right?
Like this
recommendation, you know,
[INAUDIBLE] recommends
[INAUDIBLE] pixel,
not matter where
they are, and this
matched this [INAUDIBLE] pixel.
There's no [INAUDIBLE]
really [INAUDIBLE]..
Yeah, so I think also you
maybe like philosophy things,
but you do need to keep
in mind, because they
are the people who will
pay to use our service
or pay to use our
product, right?
Because they need to have a
way to incorporate the machine
patterns with human patterns.
The preventative
medicine-- mostly we
are doing preventative medicine
before, especially in Siemens.
Prevantative medicine
means [INAUDIBLE]
for early types of detection.
And there are many
things, and [INAUDIBLE]
the all the deep
learning's protocol
is second reader,
because it means
the doctor will first read.
And then he will [INAUDIBLE]
what neural imaging will
produce, then you take a second
look on all the machine marks.
And this is causing a
problem, at least before.
And why?
Because the doctor
spends a lot of time.
The doctor is
spending more time.
So insurance-wise,
it's more expensive,
because insurance,
they need to charge
the insurance for that
software, for the actual time
they spent on
reading text longer.
You're not [INAUDIBLE] time.
You are not.
And sometimes, if the
[INAUDIBLE] amazingly well
in terms of you consider
sensitivity and also how many
marks--
the precision, you know?
The direct impression-- the
software impression for doctors
is precision, right?
Because if you miss
[INAUDIBLE] you miss, right?
The doctor will see, oh,
if you [INAUDIBLE] tumors,
only one or two are correct, and
many of the things the doctor
cannot even figure
out what they are,
and they can't
just confuse them.
These were the findings,
and doctors don't really
know how to deal with that.
So they actually are
now spending more time
without really
increasing the surplus
or increase the productivity.
This is better.
So we need to be very careful
of [INAUDIBLE] the fitter.
We all, not just Siemens,
we all [INAUDIBLE] before.
So we have to find a new way.
We have to find a
new, innovative way
to really bring
value and really help
doctors, even really
not help just doctors,
really helping patients.
The precision
medicine, actually,
recent [INAUDIBLE],,
the last two years,
I got more concrete
ideas, concrete
works going along this line.
I'm kind of like trying to
balance more from a preventive
medicine, from [INAUDIBLE],,
[INAUDIBLE] to precision
medicine.
The precision medicine,
in my opinion,
I think they are
low-hanging fruit.
There are many
low-hanging fruits.
And this is something
doctors cannot do it now,
but doctors need it.
And right now, they need
to do a lot of guessing,
and we'll fill in the gaps.
So doctors are reading
the images right
and say, oh, this guy is
severe on a certain disease,
a [INAUDIBLE] disease.
We humans, we cannot produce
quantitative measurements just
like that, right?
We don't know how much volume
or how many ml tissues was dead
or how many ml
tissues were infected.
We don't know these
kind of things.
But we can show how to make
deep learning [INAUDIBLE]..
We can produce all
these biomarkers
and all produce
these attributes.
We can count them
in metafissures
and provide to human doctors.
[INAUDIBLE] there
is no such thing.
Humans need that.
They know they need that.
Rather than they use some
surrogate, an imperfect
surrogate.
But this is the
best they can do.
So if, for example, you
detect a muscle disease.
The muscle disease--
you want to know
how much muscle degenerated,
[INAUDIBLE] to fat.
But how you measure it, how
you want it, is the same.
But right now, what
they do is they find
the doctor eyeball slides--
let's say to MRI, eyeball
slides on the lab.
The good doctor knows what's
the [INAUDIBLE] slides.
That doctor don't already know.
So that's the diffrence.
They need to do
a lot of guessing
based on that appearance.
So they find the slides.
They contour the tissue.
And then they can measure--
of course, this is
2-D. Now, it's 3-D.
So they used that
as a surrogate.
But this is the
best they can do.
They have no way to do better.
But you could say, you have
a way they can call you--
you can say they are
reading from PACS, right?
So you can compute all this
kind of things from PACS.
And PACS just give
them all the attributes
they need to fit in, where
they know what they are doing.
The different imaging
protocols were
designed for different purposes
of diagnosis or treatment
plans.
So actually, they know.
They know what kind of
conditions to compute.
So the PACS could actually call
let's say from Google Cloud
or maybe whatever
cloud, the PACS company,
they don't have the resources.
They don't have the
development algorithm
they can't do that, right?
But they need this.
SPEAKER 1: A different question.
Might [INAUDIBLE]
explain what PAC means?
Because most of the people
are not medical in nature.
LE LU: Yeah PACS is like--
PACS is the Picture Archiving
and Communicating System.
So it means like all
the images you've
done from a scanner,
no matter a Siemens
scanner, a video scanner, a
G scanner, all the scanners--
they flow in
[INAUDIBLE] from that.
And they have many different
kind of PACS companies.
They have standards.
So they can take into the
data from all the scanners,
so they store it Then they
present this to the doctors.
So they are the machine and
human interface basically.
So all doctors, you're
reading from PACS.
The PACS will push your data
to the doctor [INAUDIBLE]..
And the doctor will
read it, right?
So if you can just say--
you are archiving it,
and our hospital runs--
we diagnose the patient
on the same day, right?
So the same day, right?
So the PACS is really
the intermediary thing.
You can do a lot of
fancy things, right?
So you can say, oh, this
guys has the [INAUDIBLE]..
So he should be
[INAUDIBLE] he should
be on the cue where
he [INAUDIBLE],,
but certainly, we call
significant clinical findings.
That means you need to
call the [INAUDIBLE]..
You need to call the physician,
the main treatment then
right away, because that's
lifesaving there maybe.
It's kind of having
a [INAUDIBLE]..
So all the things you can really
inject a lot of intelligence,
a lot of computational
intelligence,
a lot of computational
things into PACS.
Of course, you have to deal
with PACS and provide them API.
The PACS will be the--
you make PACS more AI-wise or
something like that, you know?
So it's another
thing you can do.
Of course, we really call--
they call it imaging
biomarker is an indicator.
So really [INAUDIBLE] data, they
[INAUDIBLE] into attributes,
a set of attributes.
[INAUDIBLE] or
[INAUDIBLE] or something.
But this is human interpretable.
Humans know what's going
and what this number means.
And they don't have this number.
They have to guess this number.
[INAUDIBLE] this number.
I am sure they will be useful.
But you still need a-- you need
what [INAUDIBLE] [INAUDIBLE]
particle to see really if
they are efficient or not.
So this is the new
particle that should
be provided to the other rest of
the world, the other hospitals.
And our hospitals are doing a
new protocol, a new studies.
You know, we are kind
of like the last resort
for some kind of patients.
So half our patients
are cancer patients.
We work with NCI very closely.
We are basically
finding newer ways
of finding and treating patients
in that sense to save lives.
And you know, for
example, the chemotherapy,
while most of the
problems with chemotherapy
was actually eventually
in my hospital.
You know, it's not really
with me, but it was there.
I have a lot of old
people with some kind
of medical significant
[INAUDIBLE] that is next to
[INAUDIBLE].
But [INAUDIBLE] we
have in medicine.
It's more like fundamental
science, right?
But [INAUDIBLE] people,
in over this year, also
the heart and lung machine, you
know, right now, it's amazing.
You have to take the patients
heart out and do something
using a heart and lung machine
to make it still functioning,
because when the
heart is beating,
it is already
[INAUDIBLE] surgery.
So all of this-- there are
many medicine breakthroughs.
So medicine is
amazing work, and I
could imagine like
the more IT things
and more AI things can be used.
That's really kind of
reshaped the landscape.
This is the article.
This is something I want
to do, but I don't have--
I haven't done yet.
I think it's very useful.
It's patient-level
[INAUDIBLE] that it
was very useful for personalized
study, personalized treatment.
[INAUDIBLE] all the cancer--
take all cancer as an example,
all cancer patients as a unit.
Let's say you have
a lymph node cancer,
the US has about 60,000
patients every year.
But there are 600 subject,
meaning each subject is
only 100 [INAUDIBLE].
You always have a
lot of people saying
they are always personalized.
This is across the US, right?
And if you can make
everything [INAUDIBLE]
this is a new
patient, and they have
to try ABC to see if it works
or not, but in some way,
you have a magical way you
can match them to the database
and someone was
[INAUDIBLE] trait.
And use that treatment as--
kind of like you
transfer learning, right?
You find just the
closest subpopulations
of patients, how we learn
information from there.
This is not what a human can do.
And we cannot do it now.
The same topic--
I don't think we should
just do humans [INAUDIBLE]..
SPEAKER 1: Actually, I want
to dig a little bit deeper
for this precision medicine
part, because this is--
I have huge interest
in that [INAUDIBLE]..
Do you have time for a question?
Or should we save
all the question--
LE LU: I prefer maybe--
SPEAKER 1: At the end.
LE LU: I have a lot of slides.
SPEAKER 1: I think
that's a good idea.
LE LU: I have a lot
of slides to cover.
So as I say, my personal, I work
on cancer in the last 11 years,
almost 11 years, and
I work on lung cancer,
common cancer, most
common cancers,
especially in siblings.
I also work on [INAUDIBLE].
But I put a lot of effort
on [INAUDIBLE] disease,
because that's a really
understudied problem, a very--
a lot of missing links there.
They are very deadly actually.
I never worked on breast cancer.
That's not my interest.
It could be, but I
only have one life.
Also, our group, we actually
do a lot of prostate cancer.
So we do a lot of
things we actually
transfer some [INAUDIBLE] into
prostate cancer, a [INAUDIBLE],,
they do a multi-user
study, and I think actually
is one of the leading institutes
doing the prostate cancer.
So it does have a solution.
OK, so another thing, you know,
I'm by training [INAUDIBLE]..
So I always think things from
a [INAUDIBLE] perspective.
I think that's how I function.
I consider medical
imaging is a subcategory
of [INAUDIBLE], philosphy wise.
There are [INAUDIBLE]
[INAUDIBLE] different.
But it's just
actually [INAUDIBLE]..
Yeah, also three key problems--
my [INAUDIBLE] key problems--
detection, segmentation,
and text [INAUDIBLE]..
And the image capture of the
interface of text and image
is really exactly the same.
And I don't want
to go through it,
but I always talk about each
category what you can do.
And you know,
recently a year, we
started to work at two years,
well, interestingly we maybe
make [INAUDIBLE].
And this is the [INAUDIBLE]
we share with you guys,
and you guys can
share with the public.
I really feel this
is a holy grail.
You consider we have a
hospital, medium size.
We have really great
doctors [? SEAM ?]
are packed in Greece.
Greece is where the [INAUDIBLE]
information system--
so really Greece recorded
what the doctor was
writing the reports, right?
So we have [INAUDIBLE] million.
One million, high--
I think they are high quality--
very high quality reports.
And you count it, it's about 200
years doctor paperwork there,
right?
And they are the
world's best doctors.
And some bigger
hospital, maybe Stanford,
has 1,000 years and 2,000 years.
So they need to just
have more patients.
We are small.
We treat patients
for free basically.
So Stanford has 4.4
million studies.
We have about 1 million studies.
And [INAUDIBLE] had 7
million 12 years ago.
I don't know what
is the exact number.
So [INAUDIBLE] they have a lot.
There are a lot of
doctors in clinical work--
so basically, what I see is is
the treasure there is really
how we make way--
we can understand better how a
doctor was doing [INAUDIBLE]..
We already did that.
We don't need to
repeat that work.
We cannot, right?
So there are a lot of
knowledge in there.
How we can't get them and
use them to teach a computer
[INAUDIBLE] to do
deeper [INAUDIBLE]
all this kind of-- this is
the fundamental [INAUDIBLE]
should be.
I think that really what we have
done so far is the baby step.
But we're heading
toward that regression,
and before that, not many
people are doing that,
because many, many hospitals--
they have great physicians.
But we don't have people that
really understand [INAUDIBLE]
per se.
They don't have people that how
to know how to build a product.
They don't have a way of
really understand what's AI.
And I think AI actually
has some [INAUDIBLE]
any chance, so we'll everything.
So we can start doing things
like that, unfortunately
enough.
OK, so now I give
you an example.
You work with
doctors [INAUDIBLE]..
They [INAUDIBLE]
don't believe you.
So I give you a lot of pictures.
For me, it's too hard to detect
all those kind of things.
You know, I am working on
hard things, the lymph nodes.
These are swollen lymph nodes.
Why are the lymph
nodes so important,
even though [INAUDIBLE]
and we have really big, big
[INAUDIBLE].
I'm [INAUDIBLE] working
on two or three years,
we should reach a level we
can have a new protocol.
We can have new
product in a sense.
I'm still very much like
product-driven thinking.
I worked in Microsoft and
Siemens for many years.
So Siemens really
changed my thinking.
I like the way they were putting
things to work to product.
So that's really [INAUDIBLE].
Yeah, so that's a
swollen lymph node.
I won't be able to detect it.
The doctor did a
lot of [INAUDIBLE]..
The doctor finds maybe a
vessel, maybe something.
They feel this blob.
You have some kind of very
flexible [INAUDIBLE] atlas
or something you can fit in.
I don't think that any deep
learning, which is [INAUDIBLE]
because there's a
lot of reading there.
I just think that our
current neural networks
have a lot of limitations.
Otherwise, we have a
lot of limitations.
We won't be able to do that yet.
OK?
So something is going there.
But this is very
important, because this
is one of the most important
biomarkers for cancer patients.
You basically want to
know how much a lymph node
volume this patient has.
You can be the best.
You can track them
over time, because this
is a fluctuation
actually indicating
how the drug is active, right?
Because [INAUDIBLE]
plan A. You want
to see if plan A
works or not, and you
need to find out as
early as possible, right?
You wait.
You wait to see if the patient
gets thinner or something.
They have a way, maybe
blood or something,
they have a way to identify
if the A, the plan is working,
or if doesn't work,
what that means.
What's should be plan B?
What's the [INAUDIBLE]
to be plan A?
All these kind of these kinds.
Then you plan out
these kind of things.
So this can be [INAUDIBLE].
And we actually
have a physician.
His whole life, he is working
on lymph node management, just
lymph node management, and how
is that [INAUDIBLE] all kind
of different disease treatment
and disease diagnosis.
So this is [INAUDIBLE].
And I don't think
a doctor can do it.
And so basically, they need
the whole volume measurement
about how much a lymph node
this patient has over time,
but of course, for
[INAUDIBLE] detect that.
And there's [INAUDIBLE], right?
So I'll just show you this--
tnis is very early work.
This is not anything
to be [INAUDIBLE]..
But [INAUDIBLE] you have
this thing [INAUDIBLE]..
So before, you know,
Siemens used a lot of--
3-D [INAUDIBLE] Siemens.
And Siemens did a
lot of work based
on 3-D [INAUDIBLE] pictures,
and the [INAUDIBLE],,
and the [INAUDIBLE] for
this kind of aggregating.
It's one layer.
It's just one--
this is no cascade.
It's one thing.
And right now, say
we have a cascade.
Well, whatever
[INAUDIBLE] it is,
we have a [INAUDIBLE] generator.
So we basically route 99.9%
of the volume of the regions
carried out them.
We find the other
special regions.
Then we do very simple things.
We can just do like aggregations
like this [INAUDIBLE] HOG.
Even a HOG, I see my 2-D
hog, if you using it well,
it can still beat the
[INAUDIBLE] and the [INAUDIBLE]
with 3-D heart rates,
those kind of fancy things
by a large margin,
really by a large margin.
So you have to think
it right the complexity
and the decomposition.
This is a really a
decompositional protocol
[INAUDIBLE].
That should be
embedded whenever you
can to achieve
robust [INAUDIBLE]
for building a product
or hopefully building
a [INAUDIBLE] system.
So this is saying--
this shows like a--
if [INAUDIBLE] is nothing
new, but [INAUDIBLE]
is kind of [INAUDIBLE]
the kind of thing--
you know this thing
is when you have
no contour, the human,
when it is recognized,
they have [INAUDIBLE].
Maybe a human will
learn, the doctor
learn the contrast
of the contour.
So here it shows when you have
a contour detecting system,
if I integrate that into the
CAD, we will just improvise.
Of course, you can't do
everything in deep learning,
but still let's say
you can do better.
And also, this is like
a divide and conquer.
So when you get a product,
you need a lot of things
to take care of.
You really need to lay out
where you're fitting the models
and you fix it, right?
This is saying,
you know, we have--
one of the interesting
parts of CAD
is CAD is designed
to [INAUDIBLE] all
these subactionable
lesions, some
that can have big tumors,
really big human with tumors.
A CAD [INAUDIBLE] detects that.
The computer version is
easy, because we understand
[INAUDIBLE] sample, because
it's dependant on the people
population.
We're doing this for screening.
So there are maybe
[INAUDIBLE] maybe there
are only one, or two,
or three, maybe you
are like a dozen of tumors.
Tumors are humongous.
For humans, it's
very easy to see it.
But it is very difficult for
a human mean to capture it,
because it's so complex.
It's a big tumor in
homogeneous regions.
How do you learn that?
And it's a long tail.
It's a [INAUDIBLE]
with a long tail,
because all the things you
see is subtle, a subtle thing.
You are really focused on
detecting the subtle thing,
but you detect this tumor.
It's always for human doctors.
Humans don't always
[INAUDIBLE] what
the heck you are doing, how
they build trust in you.
They don't even know
what you're [INAUDIBLE]..
So how-- just I don't know
how to use that, right?
You don't feel
confident with that.
So this is something you
have to think about it.
You have to think about
how to make them happy.
Yeah, it's customers.
You make them happy, you
will make the patient happy.
We have to make them, yes.
We are supposed to help them.
That's our only goal.
And of course, you know,
we choose by simple gating,
like hard gating or soft gating,
you just build a branch system,
and you can [INAUDIBLE]
this much better.
So this is very big.
I have no idea to
detect that way, OK?
This is doctors [INAUDIBLE].
And this was missed by
the [INAUDIBLE] system.
But it's just that we have a
gating, we can get it back.
So then this is--
end of 2013, yes, November 2013
after we were getting here.
So we try.
At the time, the
major challenge is
there is a lot of people
believe neural network can
be useful in medical imaging,
because we say [INAUDIBLE]
is, well, maybe 10 patients,
20 patients, 100 patients.
We don't have enough data.
We cannot do it.
So this [INAUDIBLE] is from--
I was [INAUDIBLE] the
work in collaboratio
at the time
[INAUDIBLE] detections.
It means like you don't do the
[INAUDIBLE] at the [INAUDIBLE]
directly toward
detection, but you
do a lot of the 2-D
detections [INAUDIBLE]..
They are noisy.
They are random but you
do 100 guesses, right?
The particle [INAUDIBLE]
maybe give you
15 of them that are positive.
But [INAUDIBLE]
really low number.
It will be only 10
or less than 10.
They are all noisy,
but your aggregates
is noisy aggregating these noisy
decisions as they are clearly
statistically separable.
So you don't need to make a
prediction at the beginning,
at the low end, and you cannot.
So just don't do that
and just aggregate it.
I do think [INAUDIBLE]
and maybe much higher
than just two or three, right?
Again, it's a hierarchy
and, OK, I'm probably maybe
insensitive-- how
those categories
and how this [INAUDIBLE],,
how they are [INAUDIBLE],,
a lot of things.
But you know, say
you know, I don't
want to repeat those numbers.
They are always changing.
Another thing that we
found is the new number
indeed sometimes works
better, because the new number
is more capable in terms
of modeling things.
We can trim, you know,
in [INAUDIBLE] papers,
they all talk about a customized
test for A category and B
category.
And we found really
something we actually
can leveraging on both sides.
So this is kind of
getting into what I just
presented about the
branching of [INAUDIBLE]..
So I do think some philosophy
[INAUDIBLE] some sense.
So I have to think
how to use that wisely
and how fitting
the thing occurs.
Everything should be
statistically calibrated
and validated.
So this just shows
how human doctors do.
This is not my data.
It's really, let me see, yeah--
so human doctors has lower
sensitivity repeating
the [INAUDIBLE] thing.
So this is similar Siemens.
There was a previous
[INAUDIBLE]..
So they basically ask
a physician [INAUDIBLE]
detection first time
then a month later.
So the physician is
able to find about 15%,
3% of the funding
we used to find.
It just after months, he forgot.
He'd redo it.
But the human now has a really
low false positive rate.
So [INAUDIBLE]
who does not tends
to-- it can mean some things,
but they don't normally
give a lot of false positives.
So this is saying that--
it's complicated.
When you build a protocol,
it's complicated,
but it's saying this
is the [INAUDIBLE]..
So also, [INAUDIBLE].
Another thing is this--
this is very actually
got a lot of citations--
about 150, 160 in two years.
It's not comparable
to [INAUDIBLE]..
The [INAUDIBLE] is three to four
times bigger than [INAUDIBLE]..
And it is for a lot of people,
actually, a lot of people
are trying to see,
oh, this is really
[INAUDIBLE] not, because many
is 3-D. So sometimes it's not.
It just shows it's not
always [INAUDIBLE] to do just
everything directly from
3-D. It can be 2.5-D, 2-D,
and a flexible
[INAUDIBLE] application,
all this kind of
fuzzy statistics.
You can get something better
than just plain 3-D. This
actually, a lot of
people, a lot of-- we
as part of the papers, a
lot of people doing that.
This is one of the new
things we introduced
from an academic perspective.
Another thing at the time
people feel is of [INAUDIBLE]
that's useful or not for medical
imaging and how, and you know,
that's also-- at that time,
a lot of people [INAUDIBLE],,
but where it is small scale
like the very small [INAUDIBLE]..
They are not really big.
They are kind of as big as the
[INAUDIBLE],, as big as that.
So [INAUDIBLE] of
course, it's a lot
of [INAUDIBLE] on how
to use [INAUDIBLE],,
whether you reach conditions
would be helpful or not.
But this is work that
was done two years ago.
Right now, it seems
like otherwise.
But at the time,
it's a lot of things.
And you know, we always
keep in mind, you know,
because the company was
originally empowered
by [INAUDIBLE] models, right?
So for many specialized
[INAUDIBLE],,
you cannot train from scratch.
You get nothing.
You get crappy things.
You have to start
from somewhere.
And although this is different.
But we also show you
have a functional--
you have a functioning
maybe smart differently.
Some medical people
for long, they just
use [INAUDIBLE] models.
[INAUDIBLE]
They just [INAUDIBLE]
Fisher retractor then
when they are talking about SVM
or something to classify them.
But we do show consistently,
you do find [INAUDIBLE]..
And the later things,
actually, when
you have a lot of
data in medical,
and you are starting
from [INAUDIBLE] model,
or you're starting,
tuning from scratch,
you end up getting
similar performance.
But the human [INAUDIBLE]
[INAUDIBLE] much faster,
maybe with less,
with about half,
half [INAUDIBLE] [INAUDIBLE],,
like [INAUDIBLE] you would
need.
Of course, this is
really transferable.
And this curve, by the
way, is the best curve
I ever produced in my life.
And, you know, in
Siemens, everything
we do is what pushes the
curve to the left upper part.
That's why do every
year for six years.
And I don't think this
curve is perfect really yet.
But is it really the best
curve in my life, OK?
And we just plugged in
Ron's data for 10 years.
So that was the DOD trial.
You know, also, another
thing you can think about
is the DOD trial,
DOD, the [INAUDIBLE],,
Because whenever they have
a more advanced [INAUDIBLE],,
they are first used
for the heroes,
for the veterans, because they
deserve a lot of treatment.
My institute actually is next
to the Walter Reed National
Military Medical Center.
So the whole thing can
make it way more efficient.
You can treat the heroes
better, and it there
is a lot of things.
There are really
tremendous opportunities,
financial interests or
something like that, to really--
it's amazing.
It is an exciting era
to make a difference.
OK?
So right now, this
is not my work.
This is my colleague's work.
I am a co-author and provide
some consulting ideas, I think.
We go even farther.
So this is we're trying to do--
detect all the
plaques in our body--
all plaque in our
body, because we
have in [INAUDIBLE] about
people that are overweight.
And what's their factor
to have a heart attack?
So you have to do--
your basically need-- this
is a heart plaque where
you have to do soft plaque.
But right now, this is not
possible soft plaque yet.
But you can compute a
risk factor from this,
if you can detect, oh,
those human doctors,
right now, they don't do it.
They just don't do it.
And it's hard.
Blood can be everywhere.
They look around.
Something is always
muscle fat, bone,
because they are everywhere.
They are not a coherent
other than very just look,
they are everywhere.
They can have
disease everywhere.
So just somewhere maybe
you can have a risk factor,
you know, when you have an exam,
you know, how you manage that.
You can do a lot of preventive
and precision medicine there.
So there is really
a lot of things.
And you know, we are not
that big of academic gro9up
that we can do
things, but we have
to select a few things to
really push it forward.
But this is one of the
thing, not my work,
but it is exciting I think.
You know, also this
is have to segment
this [INAUDIBLE] plaque.
So you have to segment
it without the leaking
to the bone.
You even ask them how many
calcium in the heart's plaque
there--
what's the volume of that?
Because the volume
of plaque directly
is indicated with some kind of
risk factor of heart disease
and other things like stroke.
And it's something
we've detected,
the bowel inflammation,
bowel [INAUDIBLE]..
I was mostly working on colon
cancer detection in Siemens
actually.
You know, right now,
I work on the lung,
but the colon is
more difficult, OK?
So long detection
is, in my opinion,
it's a more easier
problem to solve.
But the patient has many lung--
many inflammations.
All these kind of
things are important,
because [INAUDIBLE] you will
know if they have inflammation.
You inject something.
And there are a
lot of things you
can do like brain
inflammation, but when
they cut the patient,
when they do the surgery,
so you have inflammation.
This is [INAUDIBLE] swallow or
this is something now working,
or this is [INAUDIBLE]
inflammation and [INAUDIBLE]..
All these things you can--
the doctor will not do it.
They don't have a way to do it.
They [INAUDIBLE].
They won't.
They have no time.
They have no resource.
So right now, [INAUDIBLE].
That's the best
protocol you can have.
I'm just imagining
you have a way
to do all this kind of thing.
You can really have a real way.
You can really have a
precision method in diagnosis
maybe in the same time
or even less time.
So you have more throughput.
You have more information
about a patient.
You really can do lots
more things than that.
I think deep learning
indeed gives a way to detect
[INAUDIBLE] [INAUDIBLE] and
a lot of things can be done.
But really keep in mind
you can always [INAUDIBLE]..
I just learned from
just the talk yesterday,
the primary talk
yesterday from GTC,
the one thing he learned
from the [INAUDIBLE] saying,
you have to go to the store
to see what's going on
in order how to help them.
You have to do that.
It's [INAUDIBLE].
You don't even know
what they're doing,
and how do you know
what best should happen?
I just give a reference,
just nothing fancy there.
I was saying, I want to reapeat
something is submitting news--
really a lot of interest.
I like this way.
I think the industry,
in my opinion,
this is only the best way.
This is probably
the only way that
would work is really the
industry and the hospital,
this should really
work or [INAUDIBLE]..
There is no other way to make
it work, because you present.
You want to change the protocol.
You want to go with
a new protocol.
You need them to know--
give you feedback right away.
IBM [INAUDIBLE] people
[INAUDIBLE] doing things,
you know?
Their strategy is right.
Whether they can
escalate the [INAUDIBLE]
is not the scope of this talk,
but that's really the only way.
There's no other way around.
You cannot get rid of
this medical institution.
You have a tremendous amount
of intelligence there.
You won't.
You have no way to--
if you feel they are not
doing something right,
we just don't understand.
We just don't why they did it.
This is the first [INAUDIBLE]
this is what we are currently
working on.
Everyone is [INAUDIBLE] on.
I think we have to be smart.
It's like what is
the problem to solve?
I would say it's [INAUDIBLE]
maybe one important problem
to solve in that sense.
It's why [INAUDIBLE]
for some reason.
Also the first, I really
think that something will
[INAUDIBLE].
But we're not ready yet.
I don't think we're ready yet.
But my goal, this my--
it's well my thing to make it
real maybe before I retire.
It really being
a magical machine
that can treat the
patient and can really--
right now, this is
really many of the--
Medicare.
They don't do that.
Even the American Cancer
Association-- you come in,
and you do [? SEAM ?]
these kind of exams.
So the experience of cancer, you
just cannot afford to do that,
because there are a
lot of consequences,
there is a lot of
effort, a lot of costs.
The costs and benefit that
occur are not that obvious.
You always have a risk, but
the costs and the benefits
is not dominating enough
you should do that, right?
So it really bringing
the cost down.
It brings the cost down.
It brings the
productivity up, right?
The cream of the doctors
right, now, the best, I do
believe they can do the triage.
They can to the triage
surgery, major diseases.
They have no problem doing that.
But we do not have
much that can do that.
We have too many
false positives.
You ask a machine to do
an exam, all the 100--
no, 500 slides,
1,000 slides, just
trying to screen
one disease, you
will see how many false
positives it will generate.
And you ask them
to do it 30 times.
And you need the multipurpose
thing to do that.
A lot of fundamental changes
need to be done there.
But I think that's the way.
We can at that time--
we've really significantly
improved the human health.
So we are looking
[INAUDIBLE] doing it online.
That's a definite long shot.
But I think we need to do that.
And also, the big data
should be fitted into that.
We should do the
multipurpose thing
starting from scratch
and building [INAUDIBLE]
everything, that's the goal.
You are kind of [INAUDIBLE] the
healthy situation will not--
the healthy population
will not be bothered,
but some patients
you find [INAUDIBLE],,
oh, you need to see this stuff.
Or you always have
this risk factor.
But you don't want really
causing the social thing,
like, if you have [INAUDIBLE]
you just scare people.
You scare people [INAUDIBLE]
or something like that.
There's a lot of social
consequence, right?
So this is my passion.
While my passion, I think
this should to be done.
And it's hard, but it does
make it that more interesting,
I think.
It just really is just too
many false positive right
now given--
you look at the current
and the last year
the [INAUDIBLE]
challenge, and you
see [INAUDIBLE] was from Google
and how long it did that.
I think that problem is
still far away from really
being useful for triage.
[INAUDIBLE]
So this is a good thing.
AUDIENCE: You're targeting very
broad triage like, you know--
LE LU: It can be
defined by [INAUDIBLE]..
AUDIENCE: More
typical right now,
because the only thing
that gets reimbursed
is super-high risk
populations, so it's
like, you know, people with high
risk for breast cancer genes,
then those you can actually
get paid to triage,
and you're looking for
something more specific.
LE LU; But you know,
cancer is random.
85% of the cancer is random.
There's no gene you can
know they are high risk.
But if it happens, it happens.
AUDIENCE: For cancer in general.
LE LU: Yeah, cancer
has 85% of randomness.
That's currently believed.
Cancer-- how cancer
causes [INAUDIBLE],,
I don't precisely
know, but my impression
is there is a lot
of randomness there.
AUDIENCE: Yeah but what
I'm saying is right now,
to the extent that there
is reimbursed screening,
it is for the subset
of patients that have
been identified as high risk.
LE LU: That's
because of cancer--
AUDIENCE: You're
typically looking
for something very
specific, because they
are that high-risk population.
LE LU: Yeah, I that
I think in a medicine
sense, you need to cover
more people, The best by ACA,
the American Cancer Association.
You need to cover more people.
We cannot cover more people,
because we cannot afford to do
that.
It's too expensive, right?
SPEAKER 1: But the
challenge really
is to get this core study over
the specific patient group
that you are interested in.
AUDIENCE: Yeah, I want
to say elder people.
SPEAKER 1: They are not
just the elder people.
LE LU: Maybe, yeah, you can--
they do long screening, right?
You have ever [INAUDIBLE].
SPEAKER 1: Yeah, start
to have the [INAUDIBLE]..
LE LU: A bad thing, it
can be a bit broader.
It definitely can be broader.
AUDIENCE: The same-- this
can be done for everybody.
Everybody.
LE LU: Because a lot of
cancer is really random.
For example, they have
a reason to [INAUDIBLE]
the healthier people.
They [INAUDIBLE] like.
They eat all kind of fruit.
They exercise a lot.
They have higher risk of cancer.
So if you are generating
this too quickly,
you have a more
chance to make error.
The RNA has more
chance to make error,
and the RNA produces
all the bad proteins.
It happens.
We don't know.
We barely know how
our body works.
We barely know how
our brain works.
This is a fact right now.
AUDIENCE: So this sort of
whole-body screen is done now.
It's available.
It's cash pay.
And so only rich people--
it's like $1,000 a year.
LE LU: It's a movie, right?
It's a movie like
the rich people
find [INAUDIBLE] rich
people living [INAUDIBLE],,
and the [INAUDIBLE]
living [INAUDIBLE]..
So they get a screen every
morning, and then [INAUDIBLE]..
You are cancer free.
You know?
In a way.
AUDIENCE: But it's the cost
and diagnosis right now
is the cost that's
actually the machine that's
giving the CT scan.
AUDIENCE: It's like 80% is the
machinery and the facility.
LE LU: But the machine
can be much cheaper.
There is [INAUDIBLE].
I think right now, the
machine is getting cheaper.
The machine you can
actually, that's CT.
CT you can scan a patient
[INAUDIBLE] a second.
AUDIENCE: It's not
[INAUDIBLE] scanning time
that dominates the cost.
You got to get this
person and changes
into their gown or [INAUDIBLE].
LE LU: You need an
approved protocol
with clear risk/benefit factors.
SPEAKER 1: Yeah,
[INAUDIBLE] the healthy--
LE LU: [INAUDIBLE].
AUDIENCE: [INAUDIBLE].
Is there an indication
that actually shows
this type of medical imaging?
LE LU: Yeah, look at the study.
Yeah, look on the patient.
Yes, you have to do that.
You might be really working
with an [INAUDIBLE] physician.
You engage in doing
that to change
the undesirable
situation we have.
[INAUDIBLE] healthy problem.
SPEAKER 1: [INAUDIBLE]
study isn't there yet.
LE LU: Yeah, we
spend like, what?
How many billion dollars?
Like $200 billion, more than
that, and it's not [INAUDIBLE]..
We can push it more.
I think we can--
given that fair amount of
money, we should do better,
I think we should.
SPEAKER 1: 10 more minutes.
LE LU: OK, so I
already said that.
I just showed you
an example of saying
this is an easy example of
the pancreas it looks like.
And I remember--
[INAUDIBLE] all my friend,
he won the
[INAUDIBLE] challenge.
That was before
neural deep learning.
And [INAUDIBLE] to see our data.
And he talked a lot how the
conclusion hard or something.
And then I showed
him the pancreas.
I said we don't know how to
solve it, and he is shocked.
He is saying, there
is really no boundary.
And the doctors [INAUDIBLE] in
the boundary and [INAUDIBLE]..
A human can't do that.
I even cannot do that.
I think we are
getting [INAUDIBLE]..
But what does that matter?
No.
I want to provide the
best measures ever
possible to the physicians.
So this is just-- this is
really an easy example.
I have many current examples.
The pancreas is
very [INAUDIBLE],,
because, you are screening
pancreatic tumors,
or pancreatic disease, sometimes
the volume on the pancreas
is very important, how
they change over time.
As a new doctor, we'll do that.
Even for doctors
[INAUDIBLE] them.
So they really need a machine
to compute the volume size,
and use a computer
perfectly well.
But the estimation should
be reasonably aligned
with a [INAUDIBLE] with
a real volume, right?
So you can really know
what they are doing.
So really, the
appearance is complex.
Look at that image.
It's really easy.
The pancreas-- how you know
this to [INAUDIBLE] other two
are not, other blob or not.
AUDIENCE: So if the
doctor don't know--
LE LU: Definitely not.
AUDIENCE: How to do
that-- how do you
teach the machine to do that?
LE LU: Yeah, the
doctor knows that.
AUDIENCE: The doctors know that?
Even though the doctor
cannot see that.
LE LU: The doctor knows.
The doctor knows.
Of course they do.
This is [INAUDIBLE].
Of course they do.
AUDIENCE: The doctor is
capable of solving what's what?
LE LU: The doctor is amazing.
I have to say, this
is the best doctors.
They are amazing.
They were trained in
that way for a reason.
You know, we sometimes
with deep learning,
you will be the [INAUDIBLE].
AUDIENCE: So where
you can't afford it.
Is the doctor
can't take the time
to take a mouse and [INAUDIBLE].
AUDIENCE: So [INAUDIBLE].
Don't have enough data
to train [INAUDIBLE]..
LE LU: I will not always
say it's a data problem.
I really think
it's not just data.
SPEAKER 1: It's the diversity
in care quality across nation.
So different regions and
different caregivers,
they don't have
a consistent care
standard across the nation.
I think that's the issue, if
I understand you correctly.
LE LU: It's a
different [INAUDIBLE],,
say, you know, I
will say you know,
Facebook and Google--
you know, you
have a lot of data You are
pushing your [INAUDIBLE]..
You used to have 30% coverage,
and now you have 60%.
With the 60%, more money, right?
For medicine, 30%, 60%, trash.
It's just trash.
You have to push to
the limit that it can
be really useful for doctors.
Then you produce a lot of noise.
They don't like it.
And when you're getting there,
I do believe [INAUDIBLE] data.
Data, of course you
need to have data.
If you don't have data, you
have nothing to work with.
So [INAUDIBLE] open with--
to help you realize that
not beyond data is an issue.
They are mainly learning--
they are many interesting-- you
say all this data issue.
We don't even have
[INAUDIBLE] anymore, right?
We just have this
[INAUDIBLE] of doing
all the [INAUDIBLE] things.
There is need for a
[INAUDIBLE] exist.
It's not just data.
It's a lot of research.
[INAUDIBLE] somehow
just amazingly stupid.
So I quit listening to them.
Humans, they do [INAUDIBLE]
even better than [INAUDIBLE]..
So my daughter, she is--
I do some [INAUDIBLE]
on her, because she
was a year and four months old.
And I spent a month at
home taking care of her.
And I started out, I
was like [INAUDIBLE]..
So she has one dog [INAUDIBLE].
We have a neighbor.
He has a dog, and it's one dog.
The dogs are running
around, and she saw the dog
and realized that's a dog.
Then the other day, I told
her to see the doctors,
and there was a
[INAUDIBLE] a dog.
It was only the back of the dog.
And then my doctor said,
oh, [INAUDIBLE] it is a dog.
That's a dog.
I ask [INAUDIBLE],, he
had a point on that.
He's not speaking--
fully speaking yet.
And that is like, I
have a different child
came in with seven
birds, just seven birds.
It was a static seven birds.
Another thing is like a Western
painting about a big bird.
Really a [INAUDIBLE]
big, beautiful bird.
The [INAUDIBLE]
different kind of bird
with Chinese different
painting styles,
she has only
[INAUDIBLE] trained.
And there is no localization.
There is a way to
supervise everything.
I just tell her there's a
bird, and she figured it out.
She knows that's the bird.
And we have a lot of things.
We barely know how
our brain works.
Our brain is very powerful.
For certain types of
motion, [INAUDIBLE]
human, not just [INAUDIBLE],,
even [INAUDIBLE] human.
Why do we care?
So we are close to solving the--
we have a new efficient
neural network.
We can solve-- we will
solve like-- we're
just a [INAUDIBLE].
We also solved the [INAUDIBLE].
This is also one of our
areas we're working on
is we really want
generally a [INAUDIBLE]
of all the different
type of effect of disease
and what volume we have.
The doctors see it.
The doctors know how severe.
The doctors give a guess
of how severe it is.
But we want to [INAUDIBLE].
And also, we keep on
working on the pancreas.
We have the best model
actually we have is not
the 3-D [INAUDIBLE].
It is [INAUDIBLE] with
the [INAUDIBLE] something.
And we get the
even better result.
But right now, we're
working on just give it up.
Right now, we're working on
fully annotated 100 cases,
how we can without
a fully segment--
without doctors fully
segmenting the pancreas,
how do we push it to be
2,000, 1,000 patients?
How is that indicated?
Maybe you [INAUDIBLE].
How do we do that?
And this is the next thing.
You do things [INAUDIBLE],,
because that--
whether you to use
[INAUDIBLE] for every--
say [INAUDIBLE] solution,
you ask them to call.
Yeah, you have no
[INAUDIBLE] about what
the patient [INAUDIBLE] use.
You want to work for every case.
If not for every case,
then 95% of cases, right?
So you have [INAUDIBLE].
So and when [INAUDIBLE],, I
never train a neural network
based on the 3-D shape.
[INAUDIBLE] worries so much a
lot from patient to patient.
So you have to
keep this in mind.
I never do things
that will overfitting.
That will-- really,
we know it will work.
You don't even need data to
tell you that [INAUDIBLE]..
You'd know that [INAUDIBLE],,
because that's why we need us,
why we need humans.
We make the decision.
We cannot try all
the possibilities.
We train [INAUDIBLE],, so we know
what's the best practice to do
and what's not.
And pictures-- we spend a
lot of time on doing a lot.
But I can cover it in a second
talk, because long, long queues
are forming [INAUDIBLE].
And the [INAUDIBLE]
queues [INAUDIBLE]..
I don't think we can save
all the lung patients,
but say we save half.
That's [INAUDIBLE].
That's more than
people die on the road.
So what you need to realize,
the issue is really the data.
The data is one major issue.
So I'm very happy we
collaborated together.
We pushed this.
I really got a lot of
emails about when and where.
I think this also the same
for [INAUDIBLE] these efforts.
I think that's great.
And she got $4 million
for doing that.
And we could hire a
contractor to do that.
I'm just kidding.
And another thing,
I have to, you know,
for [INAUDIBLE] company--
this is what-- you know,
for people [INAUDIBLE] say,
the [INAUDIBLE] keep learning
how you should work on it.
SPEAKER 1: [INAUDIBLE]
questions [INAUDIBLE]..
LE LU: OK.
So this from [INAUDIBLE] I
think a lot of people know.
It's just saying
there should be no
[INAUDIBLE] regarding the next
15 years, which [INAUDIBLE]..
I think [INAUDIBLE].
Yeah, I could take
some questions.
SPEAKER 1: [INAUDIBLE] GTC,
because there a lot of people
were at the GTC as well.
[INAUDIBLE]
AUDIENCE: So I have a question.
SPEAKER 1: OK.
AUDIENCE: You want
to reach some status
that you the intelligent
system can triage
the patients before you retire.
Besides--
LE LU: It's
[INAUDIBLE] year ago.
AUDIENCE: Besides
the data challenge,
what would be the most
promising or prioritized--
LE LU: [INAUDIBLE].
Precision medicine, yeah.
I'm asking really you
to switch efforts.
I think I haven't made it clear.
I talked too much about
different diverse topics.
One of the things we
kept into the CAD--
CAD is what I worked
on for six years.
But right now, I
actually am working more
on the same vision.
Everything [INAUDIBLE]
directly [INAUDIBLE]..
There's no human can do.
And humans need that.
So we do things human
need, and we cannot do it.
AUDIENCE: In addition
as an assistive tool--
LE LU: Yes, same
thing [INAUDIBLE]..
AUDIENCE: --for doctors
to triage the patients.
LE LU: No, no, no.
They will not be triaged.
Triage is a different strategy.
Triage is really-- machines do
triage with no human involved,
or humans need more
precision biomarkers.
They need less
guessing and more work.
Right now, they do a
lot of guess, right?
Why is this patient
[INAUDIBLE] guess better?
AUDIENCE: Yeah, then it gets
back to my original question.
For the triage
process, what do we
need to do as a field to
make that a real [INAUDIBLE]
before you retire?
LE LU: I think the doctor
[INAUDIBLE] working on it.
I think [INAUDIBLE] is
[INAUDIBLE] direction.
I think what we do.
We have to try something
along the lines.
But yes, I would
say, [INAUDIBLE]
really like [INAUDIBLE].
Did [INAUDIBLE] more
than once efforts,
depending on the newer
precision biomarkers--
same thing [INAUDIBLE] based
to [INAUDIBLE],, to hospitals,
to the many cancer hospitals.
They need that.
We haven't figured out
what they need, right?
Like I have something
about that--
there a new
biomarker [INAUDIBLE]
it doesn't work out, but it's
something along the line.
So there is-- because
nobody was doing
that, because it's too hard
nobody think there can be
[INAUDIBLE].
Right now, I think [INAUDIBLE].
[INAUDIBLE]
It really just every
[INAUDIBLE] you
can the representation
in [INAUDIBLE]
and turn that into
clinical attributes, used
for attributes.
I say the last thing that's one.
SPEAKER 1: That's
[INAUDIBLE] I want to add.
So in precision
medicine, so even
if people would
think longitudinal--
so the data is actually--
the fragmentation of the data
is actually very bad for
precision medicine stuff.
So essentially, if you want
to divide precise phenotypes,
we want to have like
for a patient, sort
of medical history or long type.
That's what we do
longitudinal analysis.
And we want all the
data to be here.
And then here, I want
to understand-- so here,
we talk about the
radiology, medical imaging.
Medical imaging is
one source of data.
And hopefully, we'll
be able to collect it
over a long period of
time like what we see.
Yeah, so the question
for me is how
this actually-- how the medical
imaging biomarker is actually
work with let's say the
electronic medical record
derives the phenotype-- how
they actually work together.
And how we validate the
biomarkers of just compare
the image-based evidence--
how this biomarker is
sort of validated and so
in combination with
other biomarkers.
LE LU: Yeah, I have
those design [INAUDIBLE]..
You have to.
That's a long way.
AUDIENCE: See, that's
what I'm curious about.
You have to design a
protocol to design a trial.
LE LU: Yeah, but
right now, saying
I feel we don't have that yet
how we can design a protocol.
AUDIENCE: I see.
Can I [INAUDIBLE] that means
if we don't have a protocol
to actually do that, the
biomarker we see today from
radical imaging studies is just
a hint for precision medicine
instead of a hard--
so there's now way we can
validate it at this point,
right?
LE LU: But you have do something
which was not possible.
Now, you [INAUDIBLE]
possible protocols.
So that's a really-- you
have to talk with doctors.
You have to-- they have
many, many years of insights.
This is why you have to work
with the medical physicians,
and they actually
know what they need.
They actually have
a strong [INAUDIBLE]
about what will be used
for future protocol.
AUDIENCE: [INAUDIBLE].
LE LU: You work with them.
I don't know.
I have no training.
I worked in the hospital
for more than four years.
I don't know this.
This is why you really
need to work with a doctor.
You need to really empower them.
You need to find the
visionary doctors that
think of beyond their
just daily work.
They have a wish list.
They do.
They do have a wish list.
And [INAUDIBLE] doctor.
And you are satisfied.
You didn't know the wish
list will be visible,
will be meaningful,
will be impactful.
Now, you have to get it done.
Yeah, you have the doctor know.
Ron knows many other things.
And other doctors,
fortunately, we
have really the
world's best doctors,
and they are visionary.
Their job is not just
treating patients today.
They are finding innovative
ways of treating patients
today and even for tomorrow.
So this is really a
very nice unit set up
at the edge to do
things, to conduct
this kind of meaningful work.
Yeah.
learn from the doctors.
I don't know.
SPEAKER 1: [INAUDIBLE].
LE LU: [INAUDIBLE].
It's very [INAUDIBLE]
in that sense, yeah.
AUDIENCE: Thank
you so much, Le Lu.
[APPLAUSE]
[MUSIC PLAYING]
