(gentle music)
- Hi, my name is Javier Alvarez
and I'm a principal research manager
at Microsoft Research in Cambridge, UK.
I have been in Microsoft for the last eight years.
And for the last five,
I have been specializing in healthcare and computer vision.
I lead medical imaging AI as part of the InnerEye team
and in the InnerEye team,
our mission is to democratize medical imaging AI,
to empower researchers and healthcare organizations
to build AI solutions in Microsoft Azure.
We want to advance the state-of-the-art in medical imaging
and transform it into a more quantitative science.
Medical imaging is used across
almost all the medical domains
and it's the biggest source of data in healthcare.
Medical images allow us to look inside patients
for diagnosis, but they are also extremely useful
when tracking an existing illness or for treatment.
Each year, half a million patients are diagnosed with cancer
and receive radiotherapy treatment.
In radiotherapy, we use high doses of radiation
to kill cancer cells, and at the same time,
we try to minimize the damage
on the healthy tissue and organs.
Radiotherapy planning is very time consuming.
In radiotherapy, we use a linear accelerator or LINAC
that delivers high doses of radiation on the target
moving around the patient.
In order to program the LINAC,
we need to acquire a 3D CT planning a scan,
where clinicians must delineate
or segment manually with high precision the tumor
and the healthy organs in 3D.
This can take between 30 minutes and two hours,
depending on the case.
As part of our research collaboration
with more than 20 hospitals around the world,
we ask ourselves, can we empower clinicians
with clinically acceptable AI models
that can accelerate radiotherapy planning?
This question has been addressed
in a recently published JAMA Network Open article
titled, "Evaluation of Deep Learning
"to Augment Image-Guided Radiotherapy
"for Head and Neck and Prostate Cancers".
One of the barriers for clinical adoption
of machine learning is generalization.
When we think about generalization for segmentation models,
the first challenge is that we need to make sure
the models work on unseen data,
but also to be able to scale the models
to work for different cancer types and body parts.
To try to overcome this,
we have worked with hospitals around the world
in Australia, Europe, New Zealand,
North America and South America.
We divided their data into main sites
and external sites for evaluation.
We then try to train machine learning models
for prostate cancer, with 519 images
and 242 images for head and neck.
The segmentation model architecture that we use
is a state-of-the-art U-Net 3D,
with 39 million trainable parameters.
We enhanced the model in many ways,
including image sampling functions
that are learned using a strided convolutions
instead of max pulling.
Up-sampled tensors pass through nonlinear activations
before concatenations,
to increase the representation power of the model.
We also added residual connections
on each consecutive convolution
and finally dilated convolutions in the encoders
to capture large contextual information.
We also had to do many distributed training optimizations
in order to train our assemble models.
We use actual machine learning services
to parallelize training on V100 GPUs
using five virtual machines.
We also implemented model paralyzation
and mixed precision representations
to be able to fit really big patches during training,
and to be able to run inference on the full volume.
In order to decide the clinical applicability
and performance of the models,
we compared the output of the model with three experts,
contouring 10 images for each radiotherapy domain.
We found that the model performance
within the human experts viability using a statistical test.
Also, we tested on external data sets
from three clinical sites.
These sites had different imaging protocols,
CT scanners on patient preparation.
We also found that the model performs consistently
on main sites and external sites,
making us confidence on the generalizability of the model.
Finally, we measure the clinical value of our models
in terms of time reduction.
For head and neck,
we found that we can reduce the time
from 73 minutes to five minutes.
This is more than 90% time savings.
If you want to try the results from our research,
all our code is now open-source.
You can go now and download it on github.com.
Our open-source toolkit allows you to reproduce
our state-of-the-art models for radiotherapy segmentation.
We also have models for classification of 2D and 3D images,
as well as models that take a sequence of images
to generate a prediction.
Our toolkit is built
on top of Azure Machine Learning services
and allows you to leverage it to scalable GPU clusters,
as well as follow the best practices
to build robust AI models with MLOps.
Our toolkit is built on top of PyTorch,
but provides configuration templates
for different clinical scenarios
that can help you build useful models
with minimal cost.
It can be as simple as uploading your data set
to Azure storage,
then using one of our configurations
and running your training on Azure Machine Learning.
If you want to work as a team at the scale,
our toolkit builds on top of Azure Machine Learning services
to make sure your models have a producible,
traceable, deployable.
And we also offer modern comparison.
Using a statistical testing to check
that when you change your data or your code,
your models are always getting better.
The models trained with our open-source toolkit
can be deployed and integrated
with clinical daikon workflow seamlessly.
Usually we configure the scanners or the clinical database
to forward images to the InnerEye gateway,
and the gateway can filter and select the correct model
depending on the image (indistinct) date.
It can also perform an anonymization
and send images to Azure for inference on GPUs.
Once the segmentation is ready,
the gateway will send the contrast back to the database
and clinicians will be able to review
the output of the model
and use it for radiotherapy planning.
Depending on your information governance requirements,
you can also deploy the same solution
on premises using Azure Stack Hub.
To learn more about InnerEye Project,
feel free to visit the InnerEye website.
You can also take a look at the InnerEye GitHub repo,
where you can download our code.
If you want to check the latest news about the project,
please go to the research blog.
And finally, you can also read a recent clinical paper
published in JAMA Network Open.
We look forward to researchers and healthcare organizations
to build on top of our toolkit
to ultimately benefit patients around the world, thank you.