Hello? Hello? Hello?
Okay, Eva, thank you. Hi, everyone, thank you for attending this month.
Webinar imaging and Informatics coming to webinar. Uh, it is my pleasure to.
Later introduce Dr and a fantastic topic that he is offered to speak on.
This is an agenda for today after the main presentation, and we'll have updates from and repositories.
Form and by the announcements next slide please.
So, it gives me a great honor to introduce Dr Simon's locker with the inaugural chair for machine learning and senior member of additional.
At the market cancer center as well as a distinguished scholar at that institution today, he's gonna speak to us about artificial intelligence and machine learning trends in oncology and the rugged path towards the clinic. It sounds like a very interesting topic and we look forward to.
During your presentation, please, thank you very much given us through the pleasure and privilege to be talking to you and thank you very much for this opportunity.
I'll be having a.
Conversation with you with regard to this topic, um, about the role of a ai machine learning and oncology and, uh, how we can actually transition from the current state, uh, web.
There is a lot of research and excitement done on this area. However, there is little being done in the clinic. Actually there is less than 5% implementation across all, uh, medical providers in the United States.
I know many of you are familiar with this topic or this crowd, but just for re orientation I start what is and, um, well, you can look at this Venn diagram shown here.
It's pretty much show the relationship between artificial intelligence machine learning and deep learning artificial intelligence originated in the 19 fifties, and the idea is to build machines that can think like, humans there are examples in consumer electronics.
There are examples of robotics and of course, self driving car machine learning. On the other hand, the.
Originated in the 960 s IBM uh, primarily the term was coined and it is the computer algorithms, the brain that drives, uh, these intelligent type systems.
Um,
deep learning is actually a newer version of machine learning is a subcategory that gets started in the 19 seventies,
and the idea was based on utilization of neural networks to learn directly from the raw data extracting feature in a more straightforward.
None of this is the common example that's used in convolution of neural network.
Now, just to dig deeper on this relationship and a transition in machine learning from conventional and shallow learning processes into the deep learning approaches in traditional machine learning.
The idea is you have, uh, some, uh, data set. You are going to extract some features. Uh, then there is a feature selection process, then you will drive, uh, some learning algorithms. Uh, this could be a, an classifier could be a neural network.
Could they support vector machine decision here? You name it, and the idea is to relate this input data through the output labels. Alternatively, these steps can be all combined into 1.
Step, which is like, the deep learning algorithms idea most commonly it's done by neural network, but can be done by other algorithms as well. This is an example diagnostic from images. Region of interest is being extracted.
The feature extraction is, in this case is done by the conclusional, uh, layers and the feature selection, or the data reduction and process done is done by the pooling player in imaging.
Pretty much we are focusing on special invariant type, uh, characteristics. Now, there is the learning test, whether it's a classification and detection in this case, trying to differentiate between what's and benign.
This is a recent addition of a textbook that gives more details into these topics for interested reader.
Some of the common, deep learning architecture there is a multi layer for spectrum, which is the most, uh, traditional version of a neural network confusion and you are the earlier recurrent neural networks.
It's actually,
when you start dealing with time auto encoders,
when you start dealing with,
uh,
typically unstructured type,
uh,
data,
and also doing examples of the noise in showing an original image here and removing the noise,
a more recent advancement was,
the invention of generative advanced neural networks which is actually pitch to neural networks again against each other try,
uh,
to fool a discriminator,
which unable to distinguish between what's real and what's fake.
And this is used the commonly for generating of synthetic data,
for example,
a genetic of synthetic city from now,
a key question to this conversation is why we should care about in oncology.
Okay. Actually, you can pick up your favorite julen in that regard.
So something about the New England Journal of medicine, there was this article, or the role of machine learning in medicine by Jeff Dean and colleagues at, uh, from Google and colleagues at a, at Harvard universities.
And the definition is about being a fundamental technology that can actually, uh, transform and exceed the capacity of the human brain. Uh, however, by 2019 re, emphasizing the point I mentioned earlier that there is a little done actually in healthcare.
So why, that's the case now, there are examples of application of the potential of artificial intelligence and cancer research, uh, talking about some of those obstacles 1 of these obstacles actually mentioned here. And which would be key to our conversation.
Uh, today is about data availability of high quality and data sharing procedures as, uh, you know, these algorithms tends to be quite data hungry. Now.
Um, this is, uh, from Lance, looking at what would be a, the key, uh.
Taking some examples from the world I work in, which is medical physics, radiation oncology this is practically the work flow starting by patient consultation.
Going through planning target definition, simulations, quality assurance, treatment delivery and follow up.
And if you are wondering where machine learning applies,
uh,
across this workflow,
pretty much in every area,
along the way,
whether it is,
uh,
automating the segmentation,
uh,
removing,
uh,
artifacts or designing adaptive example,
doing quality assurance,
or personalized treatments and trying to predict patients who are low risk versus high risk,
the example of doing this in radiation oncology,
using conventional machine learning techniques,
or more recently deep learning techniques,
which is growing at an exponential hurry.
There was a special issue, and the medical physics highlighting some of the applications, uh, of machine learning in, uh, in medical physics in diagnostic as well as in therapy as well.
Of the modeling is more focus area for most of what our team works on 1 can be divided into 2 types of machine learning techniques, uh, models. What's the relationship between the input?
And the output is more explicit, more transparent example of that are busy? I'm not sure I'm going to show a case of that more commonly use.
Our discriminate models, like the neural networks, uh, the other regression type technique for support vector machine where it's an input output relationship, without much knowledge about, uh, the model itself. It can be treated as a black box though.
This is actually debatable the box can be open as I can show some example of this, too.
Now, these are the more explicit, uh, machine learning techniques, like, uh, busy and networks looking at heterogeneous type of information. It's a conventional machine learning technique.
So there is a feature selection using the mark of blanket, then building hierarchal, uh, more than showing the relationship between these different variables.
Now, this is the example for non small cell, lung cancer, trying to predict radiation response, whether it's local control.
Or toxicity, interestingly enough, you can predict, actually, multiple objective, multiple clinical endpoints, showing hierarchical relationship between these different variables. These are the more traditional genomics features looking at their, uh, changes.
You can predict information Pre treatment and during treatment, and compare it to a conventional approach.
So, looking at the symmetry on trying to predict the response, you can see there is modest prediction, point 63, this can be improved significantly by including other data and definitely.
There's a significant change when you start adding during treatment information and having access to more datasets this book was led by you who currently actually joined us as a faculty in the new department.
Uh,
is an exciting era for quantitative image analysis and this is an example using deep learning techniques for predicting not only events,
but also time to these events by combining conventional,
uh,
features,
uh,
with auto encoders,
unstructured type data,
using deep learning to operate directly on the data or even combining the clinical type information for conducting this prediction and liver cancer.
This work was led by a way who's actually currently a resident at University of Michigan.
Now, uh, what we were able to do, uh, multi objectives with a base and networks and multi Webex this also can be done with deep learning. And the difference here, there is no feature extraction. This could be automated using proper architecture.
So a conclusion your network can operate on imaging information,
whether,
in this case,
it's a pet imaging information,
or the symmetric information that can be also represented by,
uh,
data now,
uh,
unstructured type data,
or a backup data,
like a genetic expression,
or,
uh,
cytokines can also be fed using auto encoder techniques,
and also you can look at multiple endpoints simultaneously and you can predict time to events as well using these survival networks.
And again, uh, conducting a comparison.
You can see that the performance of these algorithms,
uh,
is much better than the conventional techniques for looking at toxicity,
like,
or local,
uh,
control and the addition of,
uh,
more information compared to the symmetry also can lead to significant improvement.
Nevertheless, there are still ways to further improve the performance that work is done. Uh, but, um.
As soon, I'm sure you finishing residential Stanford and will be joining, uh, the 1st of Washington.
Now, we have these predictive type models what to do next 1 key component is using it for making decisions. So the predictive models themselves can tell you what's going to happen.
Optimizing decision would require another flavor of machine learning techniques is.
Uh,
reinforcement,
learning techniques looking at Pre treatment information looking at this better images before treatment or looking at what's happening during the treatment in addition to changes in gene expression or protein profile and in order to
make sense of that,
uh,
data for again this lung cancer example,
uh,
we need multiple,
uh,
neural network or multiple architectures,
working in tandem.
So you need the, um, again, in order to generate synthetic data. Since the original data is quite limited. We need a deep learning and network to predict the state of transitions.
What's the effect of an action on the profile of that patient and you need a clever search algorithms so,
instead of using Q learning,
which is the traditional algorithms for optimization and reinforcement learning,
there's a deep,
uh,
version of it that can work directly,
uh,
on the original data,
and these are the results for optimizing that decision making,
which is a changes in fractionation as we go through treatment.
The standard is typically to grey for fractions, whether you would like to escalate the dose, uh, amount or not and you can see in a solid line. These are the decision made by the algorithm versus the clinical decision.
And you can see that in these cases.
There is actually good correlation with the agreement between the,
uh,
algorithm itself and the clinical decision making so it was able to emulate or mimic these decision green means improvement and performance,
whether it is an outcomes,
whether it's better outcomes or less,
uh,
toxicities.
Um,
uh,
orange mean,
uh,
practically there was no change from the to greater fraction and red means a failure within the local control or additional toxicity uh,
here with this population of patients,
which was acquired prospectively.
You can see that. There is actually, uh, the clinical decisions were better from the algorithm, which was more or less, uh, quite, uh, conservative.
All the clinical decisions also were made by a failure as you can see here,
the computer algorithm was able to mimic what the decision what the protocol,
the clinical protocol for those escalation was but deficient seemingly after a while they started,
uh,
deviating from,
uh,
these,
uh,
procedures that they,
uh,
put forth,
I mean,
using their own experiences,
actually performing better at some instances and performing less and another extent.
So it's an open question. That's what was done by, uh, Hassan, uh, things will actually work at the National lab in Taiwan currently.
Now, in order to utilize this information in the clinic, the next steps of conducting these translations, they need to be software tools. These, uh, I'll go to finish these, uh, techniques.
To be a package, I'm taking the example of adaptive therapy. This is a software called outlet.
The idea is to take that information for developing predictive model for predicting local control or toxicity and also optimizing decision making with reinforcement, learning techniques and presented what the algorithm is.
Thinking,
uh,
to the physicians,
this is the architecture of the software is pretty much taking the input information and presenting,
uh,
output in terms of decision,
which are these prediction of outcomes and prediction of,
uh,
potential decision in this case,
increasing the amount of radiation or looking at the relationship between toxicity and local control clients,
make a sensitive as shown here.
What's interesting about the support is also showing the uncertainty associated with the prediction of which is very important. It's also important to know not only 1 is correct.
But also, when they, uh, is wrong, presenting this, uh, to the physician, and making them, uh, utilize that information for subsequent performance, the way the software work, whenever it has been Pre trained.
So, it is actually a supervised algorithm on a retrospective data. And if you presented a new information, it's going to show Instagrams from the variables that has been selected.
If you recall, the basic network these are actually the variables that were selected here. And it shows where the input information with law on this Instagram.
So, you can see where representation where that patient applies within the space of all the available information on their patients based on the original population. Now, looking at the prediction of local control and protection of toxicity.
It shows you the uncertainty associated with that, presenting it to the physician and asking them what they would like to do, continue with the same treatment, increase the amount of radiation decrease it or by how much amount. So, we can talk to 1 of the things that you can use with a tool.
Like, this is doing human computer interaction type of.
Study, so.
We're looking at start comparing what the AI assistant prediction versus, uh, no decision making, uh, with the physician.
So,
what the results initial results,
at least from,
uh,
this experiment is that,
um,
when the physician was presented with the algorithm results,
they have tendency to have higher,
uh,
those prescription compared with the,
uh,
without having,
uh,
the software in front of them.
And when they were asked about how much confidence they were about the decision they were making without a system, it was a 5.9 and with assistant score on a scale from 0 to 10 it was about a 6.9.
so it's 1 point increase in their confidence level. Now, what does that mean in terms of outcomes to the patient? Uh, this is still an ongoing type of study.
Now, it's not only about the radiation oncology, but also developed.
Techniques currently, uh, looking at the immune therapy response from a parent cancer actually recovered from, uh, the Orion, uh, networks right? I'm not talking to about 80 incentives.
This is,
um,
a sub category of these patients who had,
uh,
seek data or looking at multiple cancer signature for immune response using,
uh,
multiple cancer,
melanoma,
renal cancer lung and,
uh,
the deep learning algorithm was an auto encoder,
a survival,
deep neural network architecture.
Now,
we compare the performance of this algorithm with a score and I'm using a score based on,
um,
uh,
several,
uh,
antigen,
uh,
CD,
8 and,
uh,
sub,
uh,
type of,
uh.
Immune cells, and it seems this is also a predictive biomarker. You can see there is a significant improvement in terms of the prediction power.
A deep learning algorithm can provide,
uh,
in comparison to just,
uh,
you want to score the algorithm is looking at the whole,
uh,
transcript on in this case,
as you expect there will be a gene expression related to the immune response this is also highlighted by the human score that's no surprise.
But there are also other markers related to cancer behavior. Some of it actually related to the, uh, uh, metabolism, uh, activity, uh, cell cycle and you name it.
So there's still a an ongoing work, but the indicates that the algorithm is actually learning more about embedded information in a complex, uh, data like, uh, Tom, that can help in, uh, prediction the next step.
We're trying to identify how, uh, to optimize actually the individual treatments associated with these patients and scheduling of these things not showing some of the previous techniques as well.
Nothing presented several example about, uh, what they are and America can do over.
There are also challenges when we are taking these algorithms into application and accomplishing these,
uh,
challenges can be divided into,
uh,
data,
modeling,
challenges,
algorithmic,
uh,
modern challenges in terms of availability and sharing as well as the quality.
Um.
As well, as issues related to ethics and compliance, I'll show an example of that in terms of validation and interpretation of model descriptions.
So if the ethical challenges of data access, you can see highlighted in these news articles.
However, this is also being, uh, complicated, uh, recently, by some of the, uh, regulations, like the GDPR, uh, rules, uh, for instance, focusing on more patient production and privacy.
Understandably,
now,
in an ideal world,
uh,
there will be a centralized database that all the other all the data can be,
uh,
actually,
uh,
aggregated and the algorithms will be just applied,
uh,
develop,
uh,
big data type machine learning algorithms over there are many practical reasons technical reason as well as a regulation,
which makes it a little bit more complicated.
Alternatively,
Federated learning has been a proposed,
uh,
as an approach,
for example,
for in radiation oncology,
like the group at a master clinic among others and in this,
uh,
structure what's happening uh,
the data resides at these different,
uh,
side,
but need to have a certain representation in order to make it available for the machine learning algorithms and the algorithm actually visited the data in each site without the data.
Actually leaving these original, uh, sites.
Now, it's an exciting development in terms of ability to apply machine learning, uh, and overcome some of the data sharing issues. Although there are also challenges 1 looking at Federated, learning these challenges again could be algorithmic challenges.
None of different architectures.
The architecture I've shown,
which is a centralized,
uh,
service called HubSpot,
uh,
type,
uh,
design relies significantly on the server and ability of the server to be available the whole time and performing much of the,
uh,
bulk,
uh,
process in terms of communication.
Alternatively, 1 can have a vertical learning techniques, a peer to peer, uh, type of coordination, which is, you can select your partners and each side can act actually both as a server as a client. At the same time over.
There are some communication complexity associated with that. And there are other technologies that could be also, uh, outline there are differences in computer algorithms.
Uh, the performance the classical application is averaging, uh, Federated averaging, which has a lot of issues related to the data that I will describe in a. 2nd, and there are other flavors of this, like a adaptive gradient tried to overcome some of these challenges.
There are issues dealing with the I. T, at each side, whether you can apply a synchronous, having the system active, the port opens the whole time or being asynchronous there was latency and delays and how you aggregate that information in such a situation.
Some of the data issues, the distribution of the data.
Ideally,
an algorithm for averaging,
for instance,
it's assuming that identically distributed,
it's more or less of a Gaussian distribution,
but that's practically not the case 1 side from another can have different,
uh,
distributions and also can have the France's in terms of how the features are being represented or the labels having a data shift type,
uh,
challenges,
uh,
there are also concerned with regards to security or cyber security and this can increase,
uh,
the I need to increase the level of protection,
whether using differential privacy techniques with gas and coloring,
or using encryption.
Uh, techniques in order to.
The data not trackable anymore. We're currently working on a projects with multiple institutions, try to develop a platform for demonstrating these how we can resolve many of these issues with Dr Kareem and colleagues for early detection of liberty.
That I hope to update you on soon.
Now, there is still need for, uh, public data availability and data democratization and availability is a key.
There are infrastructure actually being, uh, asked for this national act, uh, initiative, uh, act of 2020 but also, there is a, the sorry, this is workload by, uh, Justin Kirby.
And colleagues highlighting the opportunities that are available, uh, with the, uh, publicly available data, making it, uh, possible to benchmark these different algorithms.
Uh, if it's not only the training, uh, process itself, and which would be extremely valuable.
Another complimentary initiative is,
uh,
with the medical imaging and data resource center led by,
uh,
Dr Geiger and the additional requirement here is looking at the methodology of the data as well as highlighting,
uh,
quality issue.
Once the procedures and how they can affect data sharing or data resource, modeling a ai type techniques, which will be also available currently it's being applied to corporate.
But the hope it could be extended across the board as well.
Now,
we are done with software tools as part of that initiative,
let's focus on machine learning and Federated learning infrastructure as an API using the gen,
3 data common versus the metric data repository using web services as a way for communication,
without for,
uh,
conventional,
uh,
machine learning or using a federated type techniques of course,
this communicate with a high performance infrastructure,
whether it is on premise,
or in the cloud.
And this is an example, actually, for we're applying a federated averaging approach currently for focusing on enterprise tablet and sustainability of a 19 type of predictions.
But, uh, the software itself is actually also supporting, uh, utilization of, uh, offline pathology data, which is still a work in progress. Uh, this work is led by who's a software engineer in our department.
There are many examples showing that a is actually not perfect. There are these, uh, headlines about, uh, Rachel bias, gender bias, uh, the Amazon, uh, story and so on.
And there are many example, also from the medical, uh, domain where the algorithm actually did not learn the features that the human would learn, for example, in skin cancer. It was a ruler, which was, uh, pretty much, uh, what the algorithm actually lived.
This is not what the expectation from an expert, or in case of a lung disease. Um, there was a qbr presence was and of course, 19 did not make it any easier. There.
Are many examples of unreliable models,
a key component,
uh,
actually to make to overcome some of these challenges,
or the common theme between them is the context that matters the involvement of experts in these applications,
uh,
on both on the side as well as,
in the medical diagnostic side as well as the quality of the data that are being used,
and sometimes lack of appropriate validation now,
towards developing a reliable API in the medical context.
This is an article by Dr,
uh,
Northern,
um,
which is focuses on a different aspects that can help,
uh,
improve,
uh,
application,
which is having a diverse score representative in this case,
having independent code for testing transparency,
Africa,
reproducible and quality assurance.
All terms are necessary in order to be able to go through that cycle let's start by data discovery and market launch and it's actually a feedback loop that is a continuous,
uh,
process as that overall these algorithms need to evolve as well.
Now, 1 of the important question, how can I build a more than well, theoretically, it's actually very easy. It is a balance between buyers, which is the ability to fit the model and variance, which is the model.
Reliability out of sample data, I'm finding that optimal point this is actually easier said than done because, uh, model complexity is an abstract definition.
Usually, these things are done, uh, more importantly, rather than there is, uh, uh, a well defined recipe for how it's being done. Another aspect is that how much sample size I would need.
Um, and typically this is done experimentally as well.
Uh,
there are learning curve approaches,
which is,
you know,
you can see a plateau between what's happening to you,
training data and validation data and,
you know,
that you reach,
uh,
that,
uh,
optimality point of sufficient sample size,
uh,
to achieve,
uh,
training,
uh,
requirement.
However, there are little theoretical work done on that error. So this is kind of an open area that you see, once in machine learning techniques, being able to do power analysis that's currently done with a conventional or statistical, uh, models.
Validation is a key component for machine learning techniques, just for the ability of these algorithms to overcome any data. Uh, there are several criteria that are used for multi variable prediction modeling.
Like,
the type of the criteria the focus is on a multi tier validation,
which is internal validation,
cross validation or external validation using independent data and the highest tier is actually somebody else taking your algorithm and implementing it independently on their data like a meta analysis type techniques also providing interpretation is essential
for the success.
This is an example for the previous study going through these, uh, uh, at least 4th, uh, tiers from a turnaround, uh, external type, uh, validations.
Now, recognizing the importance of machine learning and, uh, the growth in their, uh, publications. There are several checklists. This is.
Claim coming from a radiology. There is 1 called Mike Liam, coming from, uh, nature medicine. Of course, in medical physics, we have what we call the clamp, which is a checklist for and medical physics.
The idea here is just to highlight what the algorithm is able to do and focusing on repeat disability in the context of medical physics. We need to have a purpose justification for.
And hence there is some need for looking at prospective, clinical, uh, deployment of machine learning application, particularly in cancer, where there is multiple opportunities for interaction with the algorithm.
There are several example of randomized clinical trials that utilize a m. L. D. on several of these are familiar with focused on imaging um, computer vision type of applications. Other ones are actually from other areas of medicine.
However, this is also when I looked at these, uh, trials, um, try to figure out how they categorize compared more traditional approach.
This is an interesting paper, showing a deep learning machine learning, uh, traditional, uh, statistical, uh, models and tried to compare positive results versus negative results.
And you can see the deep learning seemingly have the least, uh, negative results mostly possible, which is, uh, quite a good sign.
However, we're looking at sources of potential sources of bias, in terms of selective reporting, incomplete outcomes, blending, uh, the data allocation and consumer and, uh, sequence randomization.
Um, you can see in terms of numbers, uh, uh.
That,
uh,
relative wise,
the deep learning algorithms tend to be actually a better,
but the numbers are relatively small highlighting still risk of,
uh,
potential,
uh,
bias when applying these techniques in,
uh,
um.
For a prospective, uh, type of studies the sample sizes tends to be still quite, uh, limited, which probably require multi, institutional, potentially sponsored by a C.
I or other, in order to make sure that you are conducting a high quality, high scale type, uh, studies and looking at some of these factors.
interpretability is important in multiple way in machine learning. Unfortunately traditionally, uh, there was a trade off between interpretation and accuracy. Deep learning tends to be more accurate, less interpretable decision.
Tree is tend to be, uh, more unprofitable, but, uh, less accurate. These are an example for enterprises, for liver cancer cases, using visualization techniques, graph camp.
So you can highlight the algorithm can highlight which areas of the liver actually were responsible for the outcomes. And when asked for our radiologist, it seems to be cirrhotic type areas.
The problem becomes a little bit more complicated when start to deal with multi or mix type data. It's not only visualization of images, but also relationship with all our genetic other type.
Of variables, and he'll say on his, rely on a prior knowledge in order to me adjustment whether that's performed or under perform there are many interpretively explainability techniques, showing some examples here.
And this is 1 of them is called the shared values can give you a positive or negative influence of the algorithm, utilizing a game theory type a approaches an alternative approach to, uh, a AI is what's called intelligent segmentation.
And the idea is, instead of having a brain, only you can have a brain computer plus, computer performing, uh, better than a brain, only assuming the computer predictions are positive thinking, the basic network approach.
This is the data driven approach this where the original variables when presented to or encouraged they were not satisfied that some of their favorite variables were not there and hence tried to include in for some of their variables into the build up of the networks actually resulted in this,
the predictions were similar,
because that's a data driven type approach the confidence intervals uh,
surprisingly,
uh,
title indicating that the expert knowledge or expert system knowledge pretty much can further improve generalizability,
which is a key component for utilization of these type of techniques.
And there's evidence in the literature to support that human in the loop can actually add, uh, to improvement and performance.
Quality assurance is essential for clinical translation. This is examples actually application.
Uh,
use cases for in radiation oncology,
but looking at some procedures borrowing from the traditional oncology world,
where there are 1 of our new software or hardware is there was acceptance testing,
commissioning quality assurance,
and a continuous,
uh,
quality improvement program that can be also adopted in implementation of,
uh,
as well.
Another, uh, approach that I want to touch quickly based on is using quantum computing, uh, techniques and this is about quantum algorithms, quantum theory versus being quantum, uh, computers. Um.
There are 2 reasons for that 1 reason is the data representation,
information representation,
the classical representation is 0,
and 1 is not comprehensive while we're looking at the quantum representation is actually can could be comprehensive in terms of how the data is actually being compressed represented push.
Also add another component of how to deal with stochastic type noise.
Another aspect was this there was now,
when you're dealing with adaptive type approaches,
uh,
the shooting shooting equations,
or actually an elegant solution for this evolutionary type,
uh,
behavior and,
uh,
of course,
with quantum computer,
once talking about whether you want to know whether the cat is dead or alive this is happening.
However, the theory itself can be utilized. Nevertheless, these are a quick example of quantum computing for a AML applications, looking at treatment planning and radiotherapy.
Uh, this is looking at the liver cancer treatment plan, and comparing a simulated annealing algorithm with a quantum tunneling algorithms.
And you can see in terms of performance are very similar,
but in terms of time reduction,
if there is this square root of to 40% improvement in performance,
using a classical type computer,
I mentioned the adaptive therapy looking at changes that's happening during a course of 3 months we are looking at the and their evolution,
uh,
over time and in this case,
you use a new or not what?
You are looking at time, but the loss function is using a conventional loss function, based on cross entropy. You can use, uh, uh, Tanya now in this number of decision support.
This is actually what this algorithm can shine, because they can deal with noise and specificity.
So instead of using a Q, learning type techniques and reinforcement learning, when can you replace this with a quantum, uh, type approaches, which tends to be robust, especially when you have limited sample sizes, uh, higher noise as in the cases.
This is just a quick overview just to give you a flavor about some.
These algorithm techniques that can actually complement com, classical machine, learning techniques and lead to more improvement. It's a topic on its own.
Now, some of the take home message is that ai machine learning can offer new opportunity for better understanding of ecology processes. There are some challenges without the algorithmic or data related. However, there are also techniques for interoperability and visualization using can be.
Here we are a science, a human in the loop or physics based techniques with the quantum computing that are still in the infancy but there is a lot of opportunities to investigate them for the rest of learning can help with resolving data sharing and availability.
But it also has its own challenges. There are also the tradeoff between privacy and efficiency in addition to the respective validation approaches like the checklist.
I mentioned,
like,
the cloud prospective clinical trials may be needed,
especially looking at human computer interaction or very important is the collaboration between all the stakeholders,
the data science,
the biologists,
clinical practitioners vendors and,
of course,
the,
in order to save and beneficial application of psychology by this I would like to thank you for your attention and leave you with some of these references that you may find useful.
Thank you very much.
Thank you Dr NACA. That was the great to to force review of, um.
The field, especially medical physics.
Great presentation. I want to open the floor for questions.
I can start with a couple of questions.
I make sure I know you.
You should be careful of learning curve or training, getting saturated and additional data may not be useful in certain cases. But, uh, in applications of Radiology and additional calls, you were clinical applications where, you know.
Uh, many patients are seen every day that each clinic, uh, what might be the control of active learning in development of some of these tools.
Well, um, I didn't touch base on that, but that's that's a great question.
Uh, given, uh, looking at temporal correlations as something can be exploited in order to reduce the dependency on the sample size and that can actually enrich the data. There are techniques.
That can look at this over time correlation or spatial correlation in order to reduce, uh, sample size requirements.
Some of them actually can go into future single shot, uh, looking at the, um.
Self supervised type,
uh,
techniques so there are methods that can exploit this,
uh,
kind of longitudinal data and all that reduce,
uh,
sample size,
uh,
dependency and,
of course,
improve,
uh,
robustness reinforcement learning is a type of active learning techniques because it can,
um,
update the.
The environment, uh, based on each additional sample size that you can feed into environment.
Um, there are techniques, uh, that can utilize as well as something called incremental learning or incremental learning, depending on how you look at it. Whether you have a new samples, then you can actually.
Incorporate a new sample information and modify the cost function, uh, on the fly so to speak.
So, there are a lot of these techniques in machine learning that can be used to deploy the data as it comes available.
Okay, thank you. Another question I have is with regards to, uh, data repositories.
That you showed a couple of examples, um.
And in the context of deep learning, they're generally unsupervised, but what is the significance of having annotations available for these large datasets?
In your opinion well, uh, what segmentations and another annotations.
I think it's, it's very important in terms of, um, public data sets is fundamentally important.
If you would like to have a benchmarking, uh, resource, of course, uh, developing a new algorithms, uh, would require, um, availability of such, uh, datasets as well.
Now, um, Dan was developed by a graduate student at the University of marias.
Uh, I mean, so you can think a lot of people who can have access to this data can come up with a brilliant ideas that canceled, uh, more complex problems.
If they have annotated data available for them that they can practice their algorithm, evaluate their algorithms or participate in some of these challenges.
The major resource for data currently are actually, um, phonetic applications. The, and of course, the giggled challenges that's the main source of, uh.
Uh, current development of algorithms, so there is benefits in terms of pushing forward the, the field of machine learning, because it's a dynamic field. I mean, it's just, uh.
Non stop, uh, new creative ideas that can solve a new problems.
Yeah, thank you. Um, there's a question a chat.
By go, do you want to ask the question.
I can, I can just read it how to use machine, learning, deep learning methods to dimensionally reduce.
Um, or for the dimensionality reduction.
Are there any algorithms based on which outcomes we're hearing under care for? Others can be measured.
Well, the conventional wisdom in machine learning is that there was, um, an algorithm capacity. What's called the routing capacity. You can actually have some metrics to measure it.
But nowadays, it's that, uh, that's kind of a change with the deep learning techniques, although deep learning currently inherently does.
The data reduction within the build up of the algorithm itself, in order to meet the capacity of the algorithms. So, um, in the past people use the unsupervised learning techniques, clustering techniques in order to do, uh, data reduction.
But with the deep learning approach is kind of, uh, flip that formula upside down. So the ideas of dimensionality.
The course is kind of being question and the, um.
I would stay away from data reduction techniques for more more than machine learning applications, uh, to be frank with you now, um, metrics, in terms of, you see, or outcomes.
Well, you see, it's used as a metric in some papers, and I've seen other cost function, a key component. Of course. So that's probably a little bit too technical is that the function needs to be differential.
So, if you can present the UC metric or biometric for segmentation in a franchise manner, it can be used. But if it is not the flexible, then it will create all kinds of, um, issues when you are doing training.
It can lead to vanish gradients and installations. And things like that, so that's that's need to be carefully handled.
Okay, thank you. And I'll read the last question. Um, Kim, I'm sorry to jump and we also have another question in the queue and a section of Webex. So, uh, it's probably not.
Yeah, go ahead with the other question and then I'll ask the 1 in QA.
Okay, yeah, the next question is, would you comment on any of.
The techniques that you describe that might be in the version of clinical applications.
Well, um, uh, given you mentioned, it's pretty much. The segmentation techniques are actually witnessing, uh, tremendous use. There are examples of them used in traditional ecology, using and diagnostic.
There are several, uh, FDA approved, uh, techniques for diagnostic. We are using for breast cancer. That's the example.
And, of course, the for pathology and prostate cancer, um, however, um.
Once these are implemented into clinical practice we'll see what's the feedback of that but there are tremendous opportunities for application of, uh, for, for our techniques.
We are trying to evaluate some of these, uh, prospectively the 1. I talked about with the adaptive, but we are doing it in a non interventional, uh, way. So I I think it's quite a changing.
There are more acceptance of,
uh,
these techniques,
especially in the world of automation,
like EMI segmentation,
quality assurance,
and things like that,
in terms of,
uh,
decision making or changing treatments that would require I think clinical trials.
Um.
Probably multi institutional clinical trials in order for a physician to change the, uh, treatment practice, other than, uh, automation or triaging of information.
But, uh, that that's my opinion, thank you.
Please go ahead. Um, this question is from Kenya.
Crawley how pronounces correctly he's congratulating you on the terrific talk and he would like to know since you mentioned the issue of bias and algorithms,
have you found any tools for automation of bias checking to be particularly effective?
Thank you. Thank you very much. That's that's another great question. Um, well, finding a single tool that can address, uh, all potential buyers is exceptionally hard.
The visualization tool that I mentioned are actually being used so it's under interpreter.
Ability there are many of these techniques that highlight some of the issues what the algorithm had, uh, had learned. Um, like, for example, for the skin cancer, looking at the ruler.
So that's kind of an interpretation of what's the performance, or was in the chest X.
Ray looking at the tubes, so figuring out what the algorithm is learned and comparing it with what an expert would do is currently 1 way of, uh, doing this. There are algorithms for, uh, detection of, uh.
Um,
uh,
error or outliers I mean,
the shock value is 1 example that shows you,
uh,
um,
where the algorithm is going and in which,
uh,
directions and it becomes up to the expert to look whether these variables are actually making sense or the directions.
They're, uh, taking is, uh, making sense and there are several automated tools that are presented by several of the vendors.
I mean, uh, um, what, if was by Google or among other ones that's being developed, but 1 solution for all of these problems uh.
Is yet to be developed, and I don't expect to be a 1 solution for, for, for all of these. So, uh, bias can be, uh, explicit.
Uh, this is what these algorithms are actually detecting, but could be implicit and this would require some experts to look at. Uh, what the algorithm is actually doing, and trying to have, uh, benchmarking for it.
If the answer is obvious, and that's can be detected. But the answer is obvious. It's kind of an open question, but in terms of experience with techniques can.
Help in that regard, and many of these servers are evolving.
1 of the questions is,
um,
related to this that sometimes in a machine learning algorithm development bias is injected intentionally in order to make the algorithm work.
So, if you have an imbalanced dataset, uh, and you are trying to make a prediction on.
A minority group versus the majority group, you are injecting bias in order to make it work. Now, the bias you are intentionally putting on the algorithm, you kind of question whether okay.
Is this actually a real, or I need to figure out, uh, another way to control the bias that I'm introducing to counter the original, uh, bias in the data.
So it can become a little bit circle logic where you draw the line between what you consider bias.
And what is, uh, actually, uh, natural in these data sets things like gender and, uh, could be a very obvious example. But there are many other examples are obvious.
Great, thank you again for for great talking. And your detailed answer to questions that came up. Really appreciate it. I think we can move on with the remainder of the program shortly. Thanks. So much for the appreciate appreciate it. Thank you.
The next agenda topic is to provide you with a brief update about the imaging data comments. Uh, as I mentioned last month, we are getting more and more into the rhythm of having monthly releases.
So the data release v7 came out in February 2022 and, uh, it contains 10 new collections, it contains updates to 7 existing collections.
And the point I would like to highlight is that, uh, now, has the com slide microscopy images for all the remaining collections.
You may recall from a previous presentation we started off with, uh, providing the slide microscopy images for the lung collections.
And now the remaining collections also have the icon version of the whole slide images available.
The release that came out in February also included improvements to the data portal.
For example, users are now able to visualize and export the big query query that they used to define their cohort. And I'm just showing a screenshot here and.
Again, related to this more frequent data release, uh, the, uh, big query tables now also include the version number of, for each instance in the imaging data comments.
Uh, once again, I will post in a moment, the URL to the imaging data comments portal into the chat. So that you can, uh, take a chance and explore these new data sets and features.
And this brings us to the update.
Can you see my screen? Yes.
Okay, so I just wanted to start this month by highlighting the archive was featured at the Frederick national laboratories advisory committee with.
Um, Dr, January presented, basically an overview presentation of the archive talking about the complexities of sharing image data.
And also how supports a number of major and programs.
Data sharing needs and then.
I don't know. So if you're interested in viewing that presentation, there's a, the whole thing was recorded and it starts just after the 1 hour mark in the, in the link that I can, um.
Will make available on the next newsletter um.
I'll also post that in the chat and then we had a new radiology collection, which is the glioma image segmentation for radio therapy.
This is providing segmentation for the purposes of variation therapy targets.
And included regions of interest around organs at risk.
Um, and we also had a new pathology dataset published expert, annotated dataset of bone marrow, psychology and hematologic malignancies.
And this 1 contains over 170,000 expert, annotated cells from bone marrow smears, 945 subjects.
And then the last thing was, we continue to highlight new research activities. This is a.
A Pre printed publication that was put out recently looking at improving breast cancer diagnostics with artificial intelligence took advantage of a couple of different. Uh.
Then also the Duke breast cancer collections from from the archived.
Um, so will most of these have already been.
Published on Twitter and Facebook and LinkedIn, and then the newsletter.
That usually goes out each month will be circulating in the next day or so.
Thank you Justin for that update.
And I guess the sleeves, the announcements with 1 minute to go, um, just a reminder.
The, uh, uh, recording of today's webinar will be made available on the NCI wiki. And once again, I can copy this link into the chat for everybody.
And the next calls are scheduled for April 4th, and the presenter will be Dr Robert from them off at Kansas center and then the following call will be on May 2nd, 2022.
Great Thank you so much. Thank you. Lee. Uh, Justin Dr America for the great presentation. I like to think. Uh.
All the attendees and, uh, to see here again next month Thank you. I have a great rest of today.
Thank you. Bye. Bye. Thank you. Thank you.