Yes, so we'll end the recording will be made available.
Only, can I ask you to if possible collect the questions that they're asking they shared in case we don't we're not able to cover everything so we have it for later.
Yes, that's a good idea. I will do this. Andre. Yes, thank you.
Okay, I think we already we should.
Go ahead cave on. I'll hand the mic over to you.
Okay, Eva, I suggest we get started.
Yes, could you hear me earlier?
Okay, when can you hear me? Yes, I think I'm sorry. Okay. So yes please go ahead. We already.
Okay, great Thank you.
Hello everyone good afternoon. Good morning, depending on where you are.
Welcome to the special edition of the imaging Informatics community.
Coupon for honey program director for the cancer.
Imaging Informatics with the center for medical Informatics and information technology.
At the National Cancer Institute to hello.
Can we can hear you? Well, okay, I thought there was a question and joining me is miss only Wagner. She's with the Frederick National laboratory for cancer research.
She's the host hostess of the imaging Informatics, community webinar, and also project manager for imaging data comments. The project that will be hearing about today and I serve as the lead for the IDC.
So the presentation today will be given by.
Foundry federal doctor and referral from Brigham women's, and he has with him Dennis contemporary who will I believe present a use case.
And so without further ado alternate to Andre.
Okay, please, go ahead. Thank you. Okay. Well, let me share my screen.
And I, I'd like to mention that we do have a record number of participants, which is great to see.
Next 1, so thanks. Everyone. Can you see my screen.
Yes. Okay. So thank you very much too early. And 1 for organizing this webinar.
And inviting us to present about emission data comments, and also thanks to everyone who joined it's great. So many many friends on this call as well.
So, today I want to give you an introduction that follows the.
Uh, release of the mentioned data comments pilot that happened last month.
I will present this.
I'll present this to get this.
And I present this on behalf of the.
Our team that can post the image and data commerce consortium.
The consorcion was led by our own keenness together with everyone. I am a cookie. I have this project in addition to the most general, Brigham, we have a instance for systems, biology, General dynamics.
Excellent publishing and several other companies, academic institutions that contribute to the development of this resource.
I also want to acknowledge the contribution of the stakeholders and our.
Uh, project leads, and they for the National app deals a K1 for.
And also the contribution of the advisory boards that provide their feedback,
and to which we report our progress,
and,
uh,
coordinate our our plans I want to give from the beginning,
the acknowledgement of the entire technical team that has been contributing to this development.
So,
inherently,
so the people who haven't contributed to development,
so the outline of this presentation,
I want to have a hopeful interactive presentation where I want to tell you about emission data comments and what it is about.
But also internally the slides with the demonstrations.
And, uh, also, um, I want to emphasize that everything that I will be shared.
I shared with you is available for you to reproduce the have links to everything that I will be shown in the documentation and.
In addition to demonstrating the capabilities of the of the portal and emission data comments resource.
And then is 1 time here, who is a researcher working with a few birds? We'll talk about development of a use case for specific radio.
Study that utilizes the capabilities available in emission data comments as the last personal presentation. I want to discuss what what we're working on right now.
And I post some questions where if the receive from the community will appreciate, you letting us know if we are going in the right direction, whether it's our concerns and I hope they hour and a half overall schedule for this session.
We'll have plenty of time for questions in the end.
So, you mentioned that the common is not a standalone resource I want to start by saying that it has been established as a new component of the cancer research data comments, or C or D. C.
and C is 1 of the resources that has been established for the.
Cancer research community here by the Joe Biden won't show to cancer more effort. The idea is that we to establish a virtual, a cloud based collaborator that can be used to.
Identify relevant data sets identified tools and without having to install those tools and local resources, or download the data, I've been able to apply the analysis and.
Reproduce the status being able to share,
uh,
those results with the community,
and also develop the components that can be used not just for cancer emission research,
but also for building other comments in other disease domains.
So, this is a high level architecture of the.
Cancer research data, common sense as you can see here the.
Um, at the bottom are the, uh, data notes, and these are the, the main specific, uh, entities that are supposed to provide the interfaces to explore and interact with the data specific to a.
They are domain of expertise, so the genomic data comments note has been established as the 1st, 1, and has been around for many years now integrated K9, data comments and proteomics. Data comments have been established rather recently and emission data.
Commerce is the most recent addition to the set of notes that are included in C or D. C. so, as you can see within each of those notes, you have a request update that follows the conventions specific to the domain of the note.
Uh, the data model was also specific to the note.
Uh,
all of the notes are expected to provide for the users to interact with the data and provide the note portal that allows the user level interaction with the data and,
uh,
built enough cohorts without having to exercise the then cancer data.
Aggregators is this new entity that is not live right now but if the project to develop this component has already been funded and it's expected to.
Uh, the release and pilot mode sometime next year so the cancer data aggregator is supposed to provide this a unified interface that would allow to query those different types of notes and build cohorts of data from different domains.
In addition to notes and the cancer data aggregator, there is also this group of entities within that are called in cloud resources and these are the components that are.
Uh, intended for the user to implement the analysis foreclose pool the data.
Uh, deploy tools and provide is highly usable and, uh, accessible, um, components for performing the computation.
So, I, I, I'm talking about this in order to make sure that.
There's an understanding that emission data comments is not something that provides ever send all of the capabilities.
Including compute and I told the repository is to the user, but it's 1 of the components of a bigger infrastructure or C or D. C.
Of course, and natural question is, why would we bother to do consultation on the cloud? 1st? Of course, there's preferred proliferation of using cloud services and resources right now. But.
Uh, they're not so common in the research domain, and in this slide, some of those problems and solutions that cloud is a.
I can provide to the research community are highlighted by using the cloud.
Uh, we, uh, do not need to download and read, download data to local resources and instead have a single centralized location of the data that can be used by multiple multiple.
Can definitely the potential to simplify and enable reproducibility of analysis. The resources on the cloud are highly scalable. You do not need to think in advance how many notes in your cluster you need to allocate.
You can allocate those resources and pay as you go and use only what you need.
Of course, uh, security is a big component of the cloud. Uh, you will have to put your data.
Outside of your institution potentially,
or work with the public data sets but at the same time,
a cloud providers have thought about this very diligently and the central security can provide the shift of protection for the users of the cloud.
But at the same time, uh, arguably each solution has its own problems and cloud computations on an exception that are concerns that the cloud data would need to leave the realm of the institution.
And you will not find all of the tools necessarily that you're familiar with.
On the cloud, and it will require the effort to install them. The cost is definitely 1 also significant.
Concern because when you, uh, of course, if you budget for a local cluster or the left of that, your pasta will be using, you know, how to do this, you know, how to put this in your application budget and there are no surprises there with the cloud.
It's more amorphous and requires expert experience and expertise to understand how much to locate and what mechanisms to use to budget for these applications.
And although our cloud is intended to facilitate reproducibility.
And there are definitely a lot of, uh, several great examples of how this has been done already but you do depend on the availability of the services and capabilities, uh, that, uh.
Utilize from the cloud provider,
so I want to be very open that each of those solutions mostly cloud and on premises has its own a had when she said there's advantages and the underlying implicit hypothesis of PwC and you mentioned data comments is that the computation on the
cloud has benefits can be viable and can be attractive,
and it can be enabling to cancer researchers.
And what we want to do with the imaging data commerce is to build this infrastructure explore.
The capabilities of the, uh, of the cloud and demonstrate best practices, how it can enable a cancer research.
In a more reproducible more accessible, more scalable way.
So, Here's on this slide is the official definition of what the IDC is about. And, uh, as you can see the main message here that we want to connect the users with the data with other types of data in addition to.
Emission, uh, data with the computational tools, uh, to facilitate those tasks of reproducing bills and democratize access to data and tools for the community.
He was the 1 kind of a high level workflow of how we envision the user will interact with.
I see, uh, is, uh, the home of publicly available image and data sets that are hosted on the cloud. So, if you want to, uh, either utilize computation on the cloud, or if you just want to explore the data.
Uh, you should be able to do this with the tools that we provide you in. Uh, you cannot just explore the data by define the cohorts based on your niche use of your application. You should be able to interact and understand. Uh.
The quality of the most, the data, the images, and the limitations that the company, uh, those images to have confidence that this data is what you.
Actually need, and once you have done that by the nature of data, being on the cloud, you will have access to a variety of tools based tools that are developed to work with big data.
To further explore and analyze it in addition to exploratory tools. Uh, there are also computational tools that, uh, you can, uh, you can use such as, uh, various types of, uh, notebooks and, uh, a enabled.
Uh, where you can do both interactive and exploratory, uh, conversational analysis.
But also can easily scale, uh, your analysis to.
Uh, hundreds and thousands of cases and, uh.
Um, so, uh, and important points here, also that, uh, by by using the, uh, the.
Uh, cloud based for plus there is a future opportunity to.
Reuse,
uh,
components that are establishing best practice of how they should be converted from 1 presentation to another how to efficiently share and reference this data to make it fair feasible,
accessible,
interoperable,
usable.
And easily share those 4% pointers to the data with, with your colleagues.
1 of the most common question also that we get from the community in the past month is we already have a cancer image in archive. What is the point in IDC? What is different?
So, hopefully, from the previous slide, 6 has should should be clear that our focus is on supporting the users that want to utilize cloud based confusion and integrate.
With other components of, at the same time, we work very synergistically with CCA and in fact, we rely on Vishay to provide certain capabilities in particular. We see.
As a system can only host data collections that are completed, you identify and have been cleared by an authorized entity. And at this point on the cancer imaging archive is that acceptable entity.
From which we can receive image data. So we definitely rely on a for a data identification duration of those public collections.
Also, if you, we don't want the IDC to become.
Uh, another resource that you can, you, you're, you are encouraged to used to download the data from the cloud to your laptop. You can already do this very efficiently with and with.
Um, uh, the idea that you define your course, and then you download the, those cohorts to the cloud based for a computation.
Very significant emphasis in on the harmonization of data. Uh, so, uh, as I will mentioned later, uh, we are using for modeling, uh, of the data for, uh.
Implemented their faces to the data and, uh.
We proactively take the collections that are user has been shared with respectively and work to harmonize them into the documented presentation or also work together with Jay and the senators of the data to help them.
Share their data in such a way that it's easily harmonized and integrated with IDC, by means of implementing the, the standard.
The project was funded, so, here, the status of the approach we were funded to.
Last year and the summer last year, and as I mentioned in October, we had our 1st public pilot release, and this is an important point. We are following the agile development methodology we want to hear and listen to the.
A hit, but from the users. So what you see in this release.
Uh, is open to, uh, modifications and if you see something that you think is, should be done differently, or something is missing. We are definitely open to your feedback.
In spring, next year we plan to have our production release and at this stage definitely will be less flexible to implement changes to the infrastructure as compared to.
Now, in the public pilot release. Our focus is on radiology data.
In the future in the next steps, we are extending this to support digital pathology and also other types of data.
Uh, the main, uh, constraint we have is that the data sets we are, including an agency have to be relevant to cancer research and they have to be available publicly.
Other than that, if it's a valuable emission data set that is shared.
And has been curated, uh, we are open to discussions of including a variety of data in in our resource.
So, for now, we have 28 collections that are included. Um, and, uh.
Uh, this includes both the original collections, uh, and the results of analysis.
Our.
Pilot release includes, uh, the portal, uh, which, uh, falls the familiar interface to other notes in here to see.
Uh, curated data, both the data and files in storage pockets and the data.
In large query tables, we have support for visualization implementation of those other components. All of this is included in, uh, in our pilot release.
So, what I want to do next is to switch gears a little bit and.
Uh, show you the, uh, the IC portal and how it looks like. So.
This is the front patients you can access the site without having to log in? I will. Uh, you can see here, I have not signed in. You can see the collections that are included.
I just see here again, as I mentioned before, all of the collections right now that are included in legacy are coming from public collections of a, and our subjects that you say, data usage, license requirements.
The exploration portal, uh, gives you the interface to explore the data in terms of what is a company in those collections.
You can either configure a search to be collection specific.
Or you can search across collections and that's where the.
The data model provided by is very helpful.
We organize the various filters that we provide into 3 categories and here we brought the categorized those filters as to those that correspond to the original data.
It's the data that comes from the image acquisition equipment, direct data, which comes from either results of the data or performance analysis of the data and related data, which is.
For the most part clinical data that accompanies the emission data, but is not emission itself.
So, uh, uh, on the on the left, you you see the, uh, the filters that you can select the subset, uh, the, uh, cohort on the right. You see the content of the of the current code. Of course. So, um.
Let's say that if I want to.
Select the images that respond to city I want to select on the cases that have images.
I can further go to the right data set. I can.
Take a look, what are the segmentation that I have with the company?
I see the images, for example, I want to have the segmentation. I want to have a cohort that includes.
Cases that have images and segmentation.
And then it's a familiar interface to browse the content of the type of hierarchy.
I select the collection and.
Digital study, I can see which is not included here.
And, uh, in the right set, I can also see the summary of the attributes of the data for each of those, uh, categories. So you can see related this empty here because we don't have related data for any of the cases, and call it an hour in the current cohort.
But you can see that the derived uh, they has different, uh.
Hello.
It glues. Andre? Yeah, Andre, you're breaking up.
Right.
You you are still, you are still breaking up the audio's breaking up and Andre if you can hear us, is there a chance you can increase the font size?
On your browser, it's it's a little bit small. Yeah, I see what you mean. Yeah. Hold on. Let me but we can hear you better. Now we can leave you better now. Okay.
Thank you let me let me share just a single window, because I think it's the reason is that I have a large screen and that it's.
Is this better? Yeah.
But it, it looks yes, it looks. Okay. Uh, it was just the, uh, the writing and the portal.
Was a little bit small. Okay. Okay. Okay. All right Andre. Perhaps you should, uh, uh, stop your videos get better bandwidth.
Oh, yeah, excellent point. Thank you bill. I appreciate it.
Okay, so so I think I know the reason why my, uh, my voice was breaking up is because at the, at the moment I opened the emission study interview where we are using your footprint. We share 5 year basis and.
Every time you open the study yeah. Before you can see the image, it has to be downloaded to your computer and into the browser.
So, uh, because of that, initially you will experience hiccups as for the study, but once.
Once the complete study, if a download that it should be very.
And as I show here, you can see both the images, and also the invitations for for this image in the in their face.
We, at this time we support 2 types of annotations. Uh, it's the segmentation object. Uh, but also the.
Therapy structure set, so if I can show you another example.
If I.
Go to there is no tab and select structure set.
Cases it will see here there are 2 collections right now that have this type of data.
So, here you see the various types of and some structures that have been segmented here and for this particular collections, the annotations actually, this collection was completed by the master clinic.
And 1 of the definitely quite popular collections on K, uh, the annotations are shared, uh, both as a.
struct and segmentations so, uh, so, yeah, these are the highlights of the main highlights of the port portal, but also here, the idea is that, uh, once you define this cohort.
Uh, you don't leave it at this point and you don't, uh.
Just finished by downloading this to your computer. Uh, let me just define another cohort, which is a bit more manageable and smaller and I will show you how you can once you selected the course, you can save it.
And reuse later. So, here again, I start with selection, just city images, but then.
I will select on the sub, the subset of the.
Uh, of the corporate that has, uh.
Segmentation so, if certain, the volume, so.
Let me just you have to sort of.
Okay, so let's say here, I have the cohort though, just the 4 cases as you can see here.
So, now, if I can log in and this is the 1st time I look in here so I have the account and the.
There is no selection process to get an account you apply and you register and all you need to do is to authorize the application to use your Google.
Uh, and here I have a little hiccup because my skills are disappeared.
So, let me just reaching the right the cohort.
We already bugging issues related to hand off from the cohort definition.
After you look into the system.
But once I logged in, it should be able to save it. So this.
So, now, under my account, taken go to the list, of course, that are available here, I can go back to any of the cohorts that, uh, I defined earlier.
And once the corporate is open, I can download the manifest of this cohort.
And this manifest will contain all the information that they need to pull the data to, to basically.
So, our documentation that let me actually go from here to the limitation page, uh, in the documentation page, we have various examples how you can use the.
Uh, uh, the data available on and, uh, we have the notebooks.
Call of notebooks 1 of which, uh, demonstrates how you can download the cohort.
To to the.
So, here, I can just quickly maybe go over those steps. I need to connect to the runtime and just for those who are not familiar, perhaps with Google call up.
It's basically a hosted instance of Jupiter notebook and that hosted instances, running on the VM. That are provided for free. You can use them for free by Google.
And you can even allocate dams that have a quite capable GP use as you will see from the demonstration that Dennis will will show later. So, here, I can upload the manifest.
I can.
Calculate the path into the manifest and here I need to provide the project.
Uh, project that will be built to, um, to pull the data.
Yeah, even though the download of.
But to Google DM is free, you still have to provide the information about your project.
So here is my my 1st.
And it's a quick note box I hopefully can can run it very quickly here.
So, I just loaded as a data frame and, uh, once it's loaded.
I can syndicate against the Google here.
And once I am syndicated, I can use the yeah, the standard just YouTube.
That I don't need to install as you have seen I just clicked the button to open the Google call notebooks.
Uh, I have this notebook here and, uh, I can download the data like this. So, uh, you can you can always go back to our documentation to the call if notebook and reproduce all of this yourself.
If you're interested after the after the presentation. So, let's see.
In addition to those components that allow you to work with the data and explore the data interactively.
Uh, we also provide the data organized tables as a storage buckets in a stack and files that follow specific interventions that established.
And also the tables that contain a 99.910% of the data available in those files.
So, what I want to show you also again, very quickly is how you can.
Uh, utilize, uh, other resources other tools to explore the data in addition to the portal and to show that I will again, start with the documentation here. What I want to demonstrate is another notebook.
That we have developed and this notebook allows to.
Uh, explore the data that, uh, a companies, uh, 1 of the again, quite popular collections the idea, right?
Collection again for requisites here is that I need to have a project with the billing configured.
And we do have a video tutorial step that explain how this can be done.
It's, uh, quite quite the easy.
So you can see the VM is Initialized. I syndicate.
It's a new VM, so I need to get again.
Right so now that I have the authenticate that I can use the,
uh,
the queries SQL style queries to acquire those,
uh,
various tables with the metadata that are publicly available and are maintained by.
So, here, for example, I can query.
Uh, the, uh, this table 2.
To get all of the accounts for the images so I can.
I saw them and see how many cities serious each case has.
I can similarly query the table to explore certain attributes that is not available for example, in the portal.
And store it in this way and to also, uh.
I can configure the URL of the viewer to open the specific, uh, study or serious directly from my notebook. So that's another tool that's going to highlight. How.
You can quite easily explore and access the data in, from, from other tools, such as such as your notebook.
For those who are that,
uh,
who would like to have a more interactive way to explore the data another tool that is available is the Google data studio that allows you to build custom dashboards to interact with the data backend such as big
query.
So, here, again, I show a public dashboard that we prepared and you can, you can build something like this literally.
In a matter of less than an hour and a dashboard.
Is just to example, to show.
How the, if you look at the summary of the data available at the instance level, how different it is at the serious levels, because, and the explanation for this is for the image is serious, such a city or a model. You have multiple instances for each series.
And you kind of lose the information about a serious that includes on the 1 instance,
but you look at the serious level,
you can see that you have as many serious that corresponds to as you have segmentations,
which is kind of a remarkable observation.
So here, for example, we have the fields that are not.
And not shown in the, in the portal where you can.
I searched by the patient wait, for example, or patient age.
And, uh, these airport is, uh.
Quite responsive, it's not instant, but keep in mind that quite large data set in the back. You can, for example, do a interactive, uh, search by a headline regular expression.
To the content of serious descriptions. So if I, for example, if I search for humor.
For serious that have a tumor in the a.
Work and this is description. That's a good.
Or if you want to search by specific manufacturer or fidelity, and here, again, the dashboard is using the.
Integration with the viewer so now that I subset the data.
I can click on the specific study from the, from this table and talk on the study interview. So, the point is that you can, you can very quickly without having to install anything. You can.
Uh, uh, get a quite good understanding about what date is available.
Uh, in in the cloud, uh, uh, find relevant data, pull this data to a AVM and start, uh, your exploration computation on the data, uh, for efficiency.
So, I will go through my next few slides very quickly, because I.
I I already spent more time than I thought I would spend on this ones just to give you some idea about key decision points and architectural decisions that we made. I mentioned before that we utilize we're live and die, come for data harmonization.
And it's a very practical decision, because there is a lot of tools that implement icon and produce the icon data.
And we definitely need a data model and data standard to harmonize contributions from different different contributors as many of, you know, that can definitely be used for a storage analysis results.
And there are many collections. And you say that follow this precedent we utilize, which are fewer.
Uh, which is, uh, has very a few customizations, uh, to provide functionality in.
Uh, all of the customizations we, we did are publicly available.
It is your footprint where it pulls the data into your browser. It's not limited to radiology. It already can support visual pathology and we are working to add more capabilities to support more complex image types.
We work very closely with the way shift team, radical image in the company to maintain. So, which is 1 of the partners in IDC consortium.
And again, all of the contributions are available to the community. Google Cloud platform is the architectural foundation on the cloud.
We rely on, uh, we tried to develop our infrastructure as much as possible, uh, in, in a way that it can be, at least in theory done without utilizing proprietary components.
But at the moment, I just see definitely realize very closely on and follows the precedent of using and other components of shared.
The portal, uh, uh, is, uh, also the open source, uh, it's, uh, it's been developed by the team.
Based on their experience with the cloud resource, we use the components that are listed here to implement the capability. We already get questions about how to reproduce this and how to deploy similar viewers.
In other systems, it's definitely possible all the components are open source, although we don't have a.
A set of instructions that you would be able to easily fall and deploy it at your side. So, next I want to switch gears and pass the controls to Dennis.
Because I want to emphasize that we are not just developing the infrastructure we, in parallel to developing infrastructure, we are.
Developed in use cases that are utilized in this infrastructure and then he says 1 of our trailblazers and that in that effort.
Go ahead Ben. Can you thank you. Yes.
Perfect nice. All right let me try to share.
You sheet? Yes, yes. We can see it.
All right, so thanks, Andrea. Thanks everyone for joining us today. I'm Dennis. I'm a research caller at Harvard.
A M, short for artificial intelligence medicine program led by professor who guards which many of, you may know high have a background in signal processing and computer vision. But the team is very heterogeneous and multidisciplinary.
We are in a group of computer scientists, engineers, medical physicists. Radiologist. And this was goal is to have deploying in the clinic and out for a technology to be deployed in a clinical setting.
We need to prove that such metal, several Boston reliable. So 1 of the key aspects of this is, of course, transparency and that is why platforms like IDC can make huge difference.
The other reason why this is interesting from research perspective, is that more and more people are nowadays convinced heading dangerously towards the replication crisis.
It's actually, I'll just see a, we go by without reading a piece about this topic. For instance, the 1 link here is from.
Last week, MIT, technology view, IBC can help boost reproducibility in your church and I'm going to show you why in a couple of minutes.
So, as I mentioned before, the goal of IDC is to make the access to cancer imaging data easier for the research community. But at the same time, potentially boss to reproducibility and transparency.
To showcase our IDC could help the community me and Andre designed the proof of concept use case that I'm going to tell you more about in a moment.
So, we started our journey investigating how the tools provided by IDC can be best, used to set up an imaging analysis pipeline.
Um, we learned and we are still learning pros and cons of using the platform as well as what could be the best practices for future users.
Ultimately, as I was mentioning before we decided to accomplish this, starting from a reimplementation with published study, using exclusively the components of IDC and Google cloud.
So, the 1st study we decided to reproduce is plus medicine, deep learning for lung cancer prognostication in, which at all investigated prognostic power of deep learning, extracted imaging features in a non small cell, lung cancer patients cohort.
So 2 reasons why we choose to start from the reproduction of this starting particular.
1st,
it is well known in the field,
the testing is carried out on a well known and widely used dataset the 1 that Andrea was mentioned before 1 dataset from these other very famous paper by who guards any in the case.
You're wondering if you want to find this collection I see just navigate to the portal and select lung as Andre showed before cfdr mix is going to be. The 1st call actually will find.
Back to our use case,
what did in the paper was to design and develop a deep learning model on private data from different sites and tested on the,
with the data set to make the results available for everyone to reproduce as they also provided a train model weights and a number of other useful scripts.
Exporting ID see, we can replicate the testing of the public data set in an even more transparent way, providing the users with not only scripts and the data, but with a functioning end to end pipeline that runs completely on the cloud.
Now, say, I am an engineer, an imaging researcher, and I've trained a model that I think works great on my private data and I want to do it all date.
Uh, test it on public data. So what are the differences between doing it locally on my laptop or on 1 of my computational notes and do it exploiting IDC and why should I use ID? Seen the 1st place.
So, let me show you why so, this is what I will personally do if I was carrying out the testing on 1 of the known set my institution.
Or my laptop, I will look for the data set, that best suits our needs on and then I will try to figure out how to use the data set how the data's restructure, download the full cohort and try to make sense of it locally.
Maybe fire up my slides, sir to these slides or probably select a batch of data. I want to analyze based on some of the projects I'm looking for.
Moving on, I will make sure my environments is set up in such a way that the experiments I'm about to run a reproducible by a 3rd party.
By installing all the software, making sure that everything is compatible with every other piece of the software.
And finally, I will run the analysis and look for the best way to shut the results, make the process as transparent as possible.
So this is what the same testing pipeline will look or looks under IDC.
So the data are not downloaded,
of course,
are stored in IDC buckets,
which are organized and does accessible always in the same way,
as Andre mentioned before to select sub codes,
we can export big query queries or create filters or Henri short before and the viewer.
The trashing browser, the Pre processing if needed is carried out using using a consistent set of tools.
There is no need to set up an environment or check for compatibility, etc as everything is already taken care of by Google cola or the VMs that the user can choose to initiate.
And sharing the results is made easier by the fact that the end to end pipeline is stored on the cloud and can be accessed explored and run by basically everyone with a Google ID and some billing information as Andre mentioned before.
So, I wanted to show you real quick how adema or Google or not.
It's like the 1 the, this is the 1 that we developed for our use case.
So, as Andre showed you before, you've got a table of contents, which can be used to navigate the.
You know.
Can, uh, in such a way to be made easier.
And not going to go through all steps that we implement.
You can implement a full end to end pipeline. I just wanted to show you a couple of things that you might find.
Mentioned before.
Um, Dennis, yep, maybe if you, if you turn off your video, cause you're having a bandwidth problems.
Give me a 2nd, um.
Okay.
My screen now yes. Yep.
So,
as Andre mentioned before 1 of the great features by DC is the viewer in this case,
when you say you're exploring a cohort,
and you want to see how data look like,
for some analysis,
where you want to select a sub cohort,
what you can do you can generate well for directly in the notebook and.
You can.
Before 1 patient.
Yeah, okay.
I think it's probably lagging a bit for because I was downloading all the data. This is pretty much what Andre showed you before.
Um, and then you can data processing, these may transparent by sharing every step.
With the end user, and also you can, of course, visualize the results and all the analysis run. In this case we were just checking the, of course, this is a small sub cohort. That's why the AC, it's 1.
but again, we completed the comparison with the original results in the plus medicine paper. And, of course, you can complete many other analysis and just share them to the end user end user.
Without them, even needing to have Google account, this is the saved information that you're seeing here is available just by going on the top and opening the Google app.
And, of course, again, everyone which is provided their, they have a valid Google ID, and with some valid billing information inside, they can access, they can run the notebook.
They can modify the notebook and save it commit to the data.
And modify, etc, but back to the presentation, it's actually.
I also wanted to point out that everything that I showed you in Google call up is going to be available also in Google cloud, which are really easy to use.
And the idea is actually to provide the user with a piece of code that can be run locally, which will kick start the process of creation and preparation of the VM.
The end user will be able to access the machine either via the Google Cloud dashboard or via more sophisticated ways. For instance.
And the virtual machine will be Initialized with the right environment needed for the experiment to run the intended. So you will not need to install anything. You will find everything already set up.
And the user will have complete access to the machine through terminal sessions that can be easily respond.
And furthermore, the development environment is equipped with a graphic user interface as well. So that sharing potentially modified versions of the cory's made very easier for very easy for the end user.
And that is pretty much all from my side. Thank you very much.
Thank you so much. Thanks. Let me just grab the screen.
And I.
I will just have those slides here because the presentation will be recorded. I want to mention that this Saturday.
Current priorities for agency, we have a lot of technical, uh, since to resolve on the back end side version. The big 1 that we are working on.
We have several questions that are related to the topics that we're exploring right now the decisions about how we will do versioning are not finalized at the moment.
We have open questions related to this. We, as you, uh, understand, we have on a small subset at this point of collections that are available from.
We have a lot of work to do to convert the digital pathology data. Uh, if there is a feedback from the community, which collections which capabilities should be prioritized in terms of the data.
Available in agency, we are definitely interested in your feedback and, uh.
The use case that they demonstrated today.
It's just 1 of the use cases we are developing, we are also working on a pathology use case, uh, which is led by Andre harmeyer.
So, uh, we welcome your feedback in terms of, uh.
How we can prioritize and better do this to inform the community about the benefits of using cloud for image emission research.
And lesson I want to mention is that if you are interested to.
Explore imagine data comments, but are not ready to commit your credit card. We have free credits that we can give to you to help build your confidence and understand the capabilities and limitations of the platform.
We would definitely appreciate your feedback and we're happy to give you those 3 credits.
So if you want to know more here, the main points of contact our portal and our forum, we definitely welcome to train the form and start the discussions.
So, thank you very much for your attention. I, I am very sorry that tiger and over time we didn't plan this presentation to be.
This loan and I'm sorry if I, I was speaking too fast. That's at some point.
So, I hope we can have a conversation and the questions from the audience.
Yes, thank you Andrea. That is that was a great presentation.
We have some questions in the chat, um, before we go to that, I want to just ask anybody if they have a question that they want to speak to.
Came on, because we have so many people on the call, and we are very pleased about this amount of interest I had asked in the beginning.
To put questions in the chat, if you like, I can go ahead and read those questions and have Andre and Dennis and the other team members of the IDC team answer them.
Yeah, that would be great. I noticed that.
Some were answered, but go ahead. Please. Yes, thank you Steve for answering at least already. 2 of those questions. So, 1st, question from Daniel Rubin is the search limited to die come head us or can you search for associated clinical data or data?
Types of image notations. Yes. So.
The source that you see in the IDC portal is mostly about search and the icon header, but because we.
We use not just for the images, but also for notation, this means you can also search the annotations that are in that format. The availability of clinical data in the portal is right now very limited. We only have clinical data for the.
Collections, uh, and the reason for this is because as probably, you know, uh, Daniel clinical data is a very heterogeneous.
And it's not, it's not possible at the moment to have a meaningful interface that would let you search clinical data across collections because.
Even the names of the columns for the same thing, may may be different.
Uh, our plan right now, and this is just a day, a state of discussion.
Is to ingest the clinical data from, uh, as is for the individual collections.
And, uh, provide that data as be query tables that are companion the.
The other types of data and I just here, so you can at least search the data. Yes.
At the same time, uh, we hope that over time, uh, we will be able to make progress to harmonize clinical data and make it more consistent.
Uh, 1 of the components of is a center for cancer data.
Uh, which is tasked with exactly harmonization across notes and we have already started working with association this topic.
And the expectation is that we will have recommendations and procedures how this can be done efficiently for, for the clinical data in.
Thank you Andre, the next question is from, I hope I pronounce this correctly if not my apologies. And the question is, are there any plans to support non die, come microscopy format?
Our current plan is to have dual personality for for the digital facilitator and the host microscopy. So essentially, we would have a file that would be recognized as a valid file.
And also the same time as a valid E. T file.
Uh, I know that David Clooney is on the call, uh, right now, where he was, maybe David, you can comment on specifically is our format and whether that would be.
And the scope somehow.
Maybe David is not around anymore so so we would need to discuss how are foremost, potentially support that or maybe it's sold or just some support that's in the day. I was in 2 different places. Yeah.
Most of the whole site images that we have to deal with at the moment or format and many of the other proprietary formats are also very and we anticipate that the dual personality dotcom tip file should allow
those to be converted without any loss of pixel data or need for re,
compression of the pixel data and the meta data will be populated in the competitors,
unlike the conventional propriety formats and the extent that there's any additional O.
M. a X amount. Meditate available. We'll port that into the into dotcom as well for the more complex formats that are related to.
For example, H, stand data, those 2 are planned to be just shared as I want to. So the same strategy should in theory work for that to the extent that we have huge data sets. So that solutions, like, in 5 hours are.
Become available, we will need to reconsider that and figure out how to spread those across dotcom components in the various different sub instances in distributed archive.
Thanks again, thank you. David, thank you. Andre. The next question is again from Daniel Rubin.
And I believe Steve Piper answered it already. So, the question was, how do you know how much you will be charged for cloud operations analysis you perform using IDC and Steve basically said it depends.
So, Daniel, is that good enough for an answer right now?
Let me maybe add a few few more points to his response.
I think the answer to this question is not simple.
I think part of which will come from you developing experience using a cloud.
I think, as you start doing some limitation on the, you start seeing how much it takes to store data on the cloud.
You will have a better idea, and we'll have more confidence. But also another thing to emphasize is that.
Uh, imaging, data comments is not really the place to do the computation. That's the role of the cloud resources that, uh, this other component, or C or D. C.
And at least some of the cloud resources, and specifically.
Uh, although it provides some capabilities to give you an estimate of how much a task you want to run.
On the cloud will cost we.
Do plan to work with Sarah and the cloud resources to.
I have a better understanding how reliable those estimates are for the image and use cases. So it's an open question.
Uh, but you're not the source to ask about this and has some, sort has already been put into address on this question in.
Thanks George wanted to know if you can searching by clinical data. I believe you address this question in.
In your 1st, part of the answer. So I hope.
George, that helps you.
That was that that was George answering the question.
Oh, that was George entering the question. Ok, thank you. Both our team. Yes.
Yeah, okay. Next question is from Anna Fernandez and Anna wants to know.
Is the plan that the older fewer will only accept icon pathology? No original icon pathology vendor formats.
And is everyone off? Yes. Yeah. Sorry. Go ahead.
Okay, and then the question continues is the vision for IDC to ingest proprietary formats.
Convert to pathology format.
And then save the original or just the di conversion and.
Will see provide a translator to convert back.
To the original whole slide image format in case researchers have viewers or analytics tools.
And used the original formats, so you have to the questions.
Yes, right so let me see if I can answer. So yes, right now our plan is to take those proprietary formats and convert them into.
It is, it remains to be seen how successful will be, but we have confident that the right way to handle this. This issue.
We 1 of the reasons we use diagram is because we can leverage a variety of existing tools that have already been developed for working with the standard.
And converters into and 2 and.
From they can call state, the emission and presentation is 1 set of those tools. So there are already.
Uh, converters that can, uh, perform this task.
As we embark on the conversion of the digital data, we will.
Test and refine those converters if applicable.
And it is most definitely not our intent to lock you into dealing with a format for which there is no way to convert it into other representations.
So, most definitely we, will we plan to have instructions how you can take data in that format.
And kind of work that into a 4 month, which is except acceptable by your tool. We have these kinds of tools for the you mentioned data for notation sense. We plan to follow this holds more specifically.
I can also address that in a bit more detail more specifically. We want to make sure that to the extent that most tools use a 1 hand for not even a handful 1 at a couple of different libraries to ingest their data.
We want to make sure that those libraries are capable of ingesting the format, even though we do have to fall back to the personality TIFF as mentioned.
So, our goal is very specifically not to provide separate store, the raw data or to provide a tool to recreate the original in quotes raw data from the dotcom file.
But rather to make sure that the tools that would rely on the original data.
Are capable of ingesting the dot com data with these are the compositive path. So we have a number of library enhancement initiatives in that respect that are coming down the pipeline.
There may, of course, be proprietary tools that are dependent on a proprietary format. That is in some ways. Very peculiar. I'm not using 1 of the current open source libraries and then those will not be supported by agency.
Next question the next question is a comment.
From John us Omega he says in Andre.
Response to your question about a solid use case, it would be great to have granular.
All the way to exposing the tile server.
And the next question comes from Lawrence will the path com.
Will Windows converters be back ported into open slide or bio formats so will they be available?
Yes, definitely that that would be ideal situation to have this available in in the libraries that community.
And it's understand you ask about by a formal because we are specifically right now we are starting the collaboration with.
Jason, to extend the capabilities of by a foremost to support to better support the whole site emission.
Object so, yes, we do want to achieve that.
Comfortability.
Okay, just another comment from Jonas, the slides are locked Andre, shared with the U. R. L. where the slides can be found.
Uh, I'm sorry hold on. Yeah. Uh, I, I think that it's okay if okay, if you sorry about that? Yes. Yes.
Okay, and Steve also responded to join us again.
Join us, ask in general, how do you see the eyes in image comments in the data image? Data comments evolving? Do you see it evolving to the point where people would not be necessarily?
Would not be necessarily the environments you propose.
Would not use necessarily the environments you produce.
Yeah, that's in place. Yeah. Sorry for.
Uh, there's such a shift, so definitely, uh, I think it's already possible to use the data.
Uh, export the data using environments that we are not that developed by other people. And that's the point of showing you the notebook. The sample data studio example is just that these off the shelf tools not designed by can be used to work with the data.
So, because we want to use the open data representation as much as possible open interface as much as possible. We want to enable this.
Thank you Andre, and this brings us to the end of the questions that we have.
Okay, and just maybe 1, 1 comment related to the question from and.
Even though we use a diagram, we try to standardize as much as possible. Right now, the interface to the data stored on IDC is not available using the interface. We provide the data that can be download. The fusion is a storage packets.
A provider be quality method, a stable source. The data the reason we do not provide icon web.
Access to the data is because Google does not allow us to have a requester page that can stores. So if we open a store for everyone, it means anyone would be able to download the data. And we would have to cover the cost.
We cannot control the cost because of this when we run the viewer right now, it doesn't talk directly to the web interface with folks that can web interface through the proxy. So it is an open question and came up with some of the users already.
Can be open and come up in their face for, uh, access in the data.
Right now we cannot, but, uh, if there is a significant push from the community, and we have.
Number of use cases, and we definitely should reconsider and bring this up also to our partners at Google Cloud healthcare.
So that like a technical point, but I want to mention this.
Thank you I have 1 more question. Can you point to the top GitLab or half account with the open source of the portal?
Yes, definitely it should be in our documentation, but maybe it's not so, let me quickly.
If you mentioned data, common, sleep organization, we bill posted it already built. Excellent. I just okay. Thank you. It's twice.
And and Curtis comments, nice presentation, Andre, and the whole team Thank you for presenting today.
Thank you very much for attending and then I'm impressed. We have so many people sticking around until the end of the talk.
Thank you the team and thanks for Lee just wanted to make a couple of quick announcements.
Can I see my screen? Yes, I stopped sharing.
Do you have a slide? Yes, I can see the slide.
Okay, so I just wanted to mention that there will be a future presentation on or 2020 in a couple of weeks. It's on December. 3rd.
5 to 6 PM and.
There will be,
I think 4 presentations included in that 1 hour slot,
uh,
presentations by Andre of course,
belong about who spoke and it will speak on the cloud architecture support for IDC gigahertz on use cases.
And Brad Erickson who chairs the external advisory board for IDC. We'll talk about imaging applications in the cloud.
And the next imaging coming to webinar, we will be back on the regular.
Schedule of 1st, Monday of the month and.
It will be on December 7th, our colleagues from the National Institute.
For allergy and infectious disease doctors, Rosenthal, and heard will present on the tuberculosis by a girl.
I hope that we can join us, uh, that you can join us for this presentation on December. 7th. That's all I had a will lead to you.
I wonder if you wouldn't mind going to the next slide I just wanted to tell everybody who may be new to the imaging.
Informatics webinar we have a Google group.
Please feel free to join us there. It's NCI imaging Dash, community, Dash call and we send announcements.
Uh, about upcoming calls, we send announcements when recordings have been posted to the Google group. So please feel free to join us there. Today's recording will be posted on the the U. R.
L. is when you scroll up in a chat it's towards the bottom. I posted it there. It's also listed here under imaging, community, call wiki.
And I think that's it for me.
Thank you. Thank you. That's all I want to. Thank.
Andre, and that is for a great presentation for for.
Then everybody who attended, and for questions that were asked, that was a great discussion. Thank you and please follow us online at.
This course, thank you very much. Thank you for having us.
Thanks everybody. Thanks. Thank you. Very much guys.
Thank you. Bye. Bye.
Okay, bye. Bye.
Silence.