Okay, welcome everybody to this February.
Installation of the imaging Informatics happy, New Year and this is our 1st.
A presentation for 2021.
And it is my pleasure to present to you today. The speakers are.
From insights from your program division of.
Cancer genomics on epidemiology, um.
Ah, they are Dr Jonas Omega whose? Chief? Scientist and senior investigator chief data scientist. Sorry.
And senior investigator at D. C. G and his colleague or follow ups are.
Uh, and they're going to present on orchestration of distributed. It has archives to us.
So, I do go to Jonas, please.
Thank you.
So, the 1st thing I do is extend the list of authors so we.
When it comes to to blame, you can only blame me and profitable, but if it comes to credit, we'll have sync with Eric primer and Joel salts. I saw.
Joel on the call, I'm not sure if Eric is on the call already. It just talking by Skype in a minute ago. So.
Go ahead, I guess some voice in the backgrounds.
Yes, Eric is on. Oh, all right. So everybody is here. So what we'll be reporting on some results that start with this paper where Eric is the 1st, author.
Where we are looking for ways to traverse images in remote locations, using range requests.
So, the idea is that these images are very large. Sometimes they are all over the place for governance reasons.
Different groups are working together, but they don't want to pull the data together or they cannot. So, do we have mechanisms where we can engage full slide images data? And this is a generic problem also it's the same mechanism.
And the way browsers, sorry, service support is range request message.
That allows is just to take part of the image. The challenge, of course, is for all slide images how do we know.
How to retrieve specific position at a specific resolution so, this is a very applied result. So, the paper is here.
By the slides are public, I'll share the link at some point when I stop talking to the questions.
Come in, and so you'll be able to follow them. There are only 4 slides and I want to get to this 1. so a list of discussion points. So what does it mean that.
Range the request, the couple the images from its analysis, the governing is addressed in a completely different way of the standard approach of having the analysis, or the towels serving in the same place as the data. So we study that the serving not can be.
Somewhere in the cloud and that's going to be the opening slide right here.
How this is driven by and towards.
Artificial Intelligence modeling, and they are both issues of cost and governance that are addressed by this alternative architecture. We're presenting to you that I think are worthy a detailed discussion.
This would be more interesting when you get 5 number 5.
So, we've been moving for a while manually towards active learning manually the sense that we get our favorite pathologists to.
Annotate power select images and then we analyze these images find ways to classify them using the models and then we return the models.
And I'll show an example of the more conventional approach being extended to a different orchestration by this model. Then number 5 is we are moving because of privacy concerns, and regulatory concerns that keep mounting.
Uh, we have pretty much now limited to using solutions where we distribute the algorithm.
Not the full application itself, so the data really has to stay put. And so this is what Federated learning is about.
And there are a number of hyperphile publications out there with 2 links here.
Where you see this discounts that the data cannot be proved, they can't even the access.
What we are distributing is the linear regression.
Procedure at some point during my presentation, I think most of you will think well that's good for you but maybe not good for us.
Because we don't know what to deploy these things or we don't want to be settled.
With your choice of back end so the health service provide the service.
And so there's some good news here we signed a contract with Google professional services.
To move Eric style server after being hardened.
Into a service, Google cloud computing marketplace. Of course, this services can be made available in any cloud marketplace.
The idea here is that if you don't have friends, you can still use style server without even telling us about because this will be tight.
To your billing accounts, uh, in a completely different system we don't have to be aware of. So it's a fully portable solution. And as I go on through the.
The demonstrations, please know that I'm going to try to spend some time on the last 1, which.
Where is the, what is a conversation we want to promote is a conversation we feel.
We need to have, which is, I think these experiments provide clues about what data comments.
And need to be, and when I say need to be as for a specific classic stakeholders.
So, the original proposal data comments was about API, ecosystems.
Could you see, now, data includes emerging in the same space so I'll try to present the material in a way that.
Prepares this discussion either they can claim for example, should we treat them architecturally as on the client side?
Every data comments, rather than being a container for.
The Commons themselves, and I'm going to be arguing right to be the 1st.
Argument that if we can't access the data in a way.
That hands, and and in a way where the governance of those API calls is made by tokens, for example, using or 2, then we are in a place.
That doesn't really believe her, the goals that the comments. So try to provoke some.
Discussion at that level so this is maybe the most important slide and then we'll move to.
Demonstrations please interacting with questions. If this 1, if I'm not clear describing this image the rest of the presentation becomes a bit confusing.
So, we have image by a format which allow us to map range requests.
In 2 positions in the image pyramid of multiple formats.
So, this image from it was developed to provide this mapping for multiple, all slide images.
Slide image format, which means that a service of this nature will be able to handle all these formats.
So, we call the API by specifying the position and the scale.
This is then translate it into positions in the file location. So there is a pointer we, the beginning and the name.
This is sent to anything that is able to consume the rest API supporting. It should be range request.
Which it turns out to Eric, I think, makes his comment in the paper. Lucky for us, the web servers in cloud services. They all do. So, it's very much part of.
Of what a consumer facing storage in the cloud service done. So, this includes things like com will show an example with box. Com also includes S3 buckets.
Includes the number of these tools, and you see that there is not much difference between.
Being private, or probably, the only difference is that if the call goes.
To a private repository so the person making the API call this person as an identity went through off too.
Authorization they are talking generated all of this happens and they're under the skin in the system. It's standard in any wants to authentication system use all the time every day.
So, when you log in com, for example, this is generated for you.
So, this spirit token is associated with the call.
And it goes to a private repository, just the summary that goes to public.
I want to make sure there is no confusion here. So, the difference between the 2 columns is that the call to a private.
Repository called repository is there the authentication handler of the call carries your very token.
As far as the functionality is exactly the same public.
Or private nice discussion unfolds.
I'll try to highlight a few things that are happening here.
And here, so perfect will show the demonstration.
1st, with being used to analyze So where pathology sound, you can get an image that is a moderate size.
And then you'll jump to the whole slide image file servers. So, just highlighting what.
Can be what uses this can be made of this system 1 stays programmatic in profitability. Extremities typically rent requests.
So, for example, we could be getting the located tiles or the.
And provide them to a cloud service.
That takes care of the parameter so.
Things like, what is the topology of the neural network, for example and of course, the whites of the neural network.
Are handled somewhere else trace for learning then becomes trivial because this happens all in the same pipe.
And so we could then be returning this information here. No notice.
If you want to distribute to model itself, we could be using, for example.
And just to be distributing it through web applications.
What you see, that is common to all these systems, is that they have no length. So the moment you access them by the reference, the irrelevant web page, for example, they are all available to, you download downloads or installations needed in such a system.
And that really, from the discussion of point number 7, what image that appointments would ideally provide, includes the support for systems like this, such that we could call tiles as an image.
That is archive somewhere else then I left.
Formalize the fact that a lot of us like to work in analytic environments that we're very familiar with, like our studio Jupiter notebooks, MetLife and so on and the or sales to talk to to the statisticians.
So, all those analytical tech firms, they support and URL calls. So, currently to do it from the command line, which means that they are.
Equal players to some levels web applications.
So, as long as the call is made this way, you get the tiles, you need to do your analysis, your return results.
Where it needs to be kept, so I'll pause a little bit. See if any questions, otherwise we move to a demonstration.
Right and mutual lines if you have questions, I think everybody by now has been on has been muted in Webex. So please unmute yourself.
Okay, I'm gonna move to then. Maybe There'll be more questions. Once people see this system working.
Okay, so I'm going to start with this paper here so.
Joel is the last starting, and my part was to be built using the interface.
So, typically, it's linking to this page taking doing it because it does and it's good. Okay so we started some language to general audience and then pathology audience.
The factory spoke it here again with the link and so this promise that we are delivering.
Classifications with no lights so it's delivered through the web page so we have application.
And the words you get the typical use of the tool so you have to 1 for cancer.
When another feeling for sites, and number of students that allow the user versus to digitally dissect the tumor. So.
The background 7.
Engage in the system directly. Hopefully you can see. Yeah, you should be able to see it.
I cannot remove the cancel, I believe, for sites.
And we go straight to them. So, what we have here is the painting slide image and then us being able to identify it and this is the net scale with no downloads or installations.
So, here, we could do this format, an image.
So, the interesting thing happens when you open their tools, and you will get what's going on in the background. So, there is a tile server in this case microscope.
So this will be familiar to most of you and so if I click on the network.
In a start moving the image around we see the central no, not the step.
In these applications, let's get the preview.
So, you see some results so, numbers are coming out, but not the images.
Not times this is happening within and I frame and the tile server is placed within the service itself.
So, I'm going to move to a different system, like, to make sure I put the link.
Yep ID so the same system using a towel server.
So we can know it to and this is file again. There are lots of them here.
And the format is not just but other formats will be supported and again to open the depth of the browser. So you see what's happening.
Okay, so again, network start login.
And you see the calls coming through and I need to show you.
The same reason, I'm not seeing.
I I think you have selected.
Sorry, uh, I think you have etc selected, uh, uh, at at the, at the top bar, just above the, the waterfall yeah. Can you select all.
Here just just to the right just to right of that.
Oh, of course. Sorry. Thank you. So you see the calls coming through.
And so again, further you see additional calls.
Each of them speaking a tile so the styles can be called from anywhere. If I share this link with, you.
Which I'm doing right now.
I have to expand the size of my Webex. So I'm in trouble. I can see the tools preferably by sharing 1 of the links.
Sure, I have no idea. Right thanks to the.
The Webex controls.
All right hopefully you have a link no.
So, by using this cloud servers, we can be calling images that are in different locations. That is the main point. So, now if we go here.
And we try to do what we promised here. So this is the old system.
And now the system here, we can get to visualize it without servers. Now, the promise is that when we look at his handlers.
They could include in the authentication layer. They could include information about who the user is.
And if say, you have images in repository that support.
The orders to is the authentication and authorization mechanism, the fact that an image can be referenced or not is addressed at that level.
So, now, if I go here and put box, assuming we did a good job.
You will see that if I'm authenticated to the book. If I'm not duplicating box dot com, I should be asked to go through the last 2 days.
Can watch indicated this works all the time. So now we are going in.
And you see here that I am authenticated, so I do exist.
And this is the point where you take over, so I'll show you how you can visualize the images the same way. We could visualize them here.
You can do it in the back end of your choice where he wants to support it. And the governance is at the account level.
You ready oh, yes.
I'll start with the the okay, so before you start to the mother, it says image.
We were we have to discuss 1st, the AI component of this story. How can we distribute classifiers at scale?
And if no, no, no costs and then we'll move to the place where we are today, which is the active learning.
Based on annotate and tiles in different places.
Silence.
Absolutely having problems.
Don't as we can still hear you. Yes, but I don't see a computer.
Uh, yeah, it says starting to share content, sometimes it takes a more.
Possibly may need to add under Webex is oh, yeah, we can hear you, but we can see your screen.
I I have no idea what happened, whether it's just crashed on me. I'm trying to share again.
Not any policy database as well not surprised.
I'm not sure I think there might be 2 of me. Yeah, we can see it now. Yeah. All good.
Okay,
yeah,
so,
um,
yeah,
as as,
as you said,
uh,
because of the,
uh,
the provision and the maintenance of that,
uh,
providers like Box cloud,
sort of service providers,
like Box,
uh,
give us a out of the box pan unintended.
Um, we are able to make use of those api's and extend the functionality that, uh, that box takes directly off the shelf.
With our own ideas so, for instance, we have, uh, this, this, this viewer right now that you can use to browse through your box, uh, imaging data.
Um, as you said, this is a, a, and we can browse through this is all data from from box. I'm I'm making API calls to box that allow me to retrieve the data and show it to go here.
And what we have been able to achieve with this functionality is that we are able to provide functionality that.
That that is analogous to a pathology, uh, analysis, tool, additional pathology analysis tools, or something like Halo from collapse.
So we have a we have a section where a pathologist can come in and create their own adaptation definitions or classification definitions. Like I have here. I have, I see advocacy and tissue advocacy.
So,
this,
this is where we,
where we started off,
this is just a definition that pathologists use to specify if this image is a good quality or not if this is analyzed or not,
they,
they would,
they would come in here and select 1 of these based on their subjective criterion if they think it's a good image,
they would select a thumbs up.
They think it's not deselect comes on and then based on that, based on those annotations.
Uh, or classifications, in this case, we are able to, uh, use.
Uh, that that data that very valuable data, send it to, we use cloud or Google cloud service, which is training, uh, as as a service offering that they that they provide.
It just does some really nice hyper parameter optimization and essentially gives us a train model. Right.
Right from the data that we give it and the scores that you see that you see here are a result of that model making making a prediction.
And the, the interesting thing is, um, this is all happening on the flight. The model is not running on a server. It's running on my browser.
So, when I, when I load the page, it also loads the model with it and I'm able to run the more on the image that I'm seeing here. So, the image is on my computer, the model is on my computer and the analysis also happens right here. So there is no data transfer that is happening.
We are able to extend this a, to a new context as, as you said this, this was the 1st.
I am sorry about that, that this was the 1st active learning proof of concept that we were able to achieve. This is just a very simple 300 image data set of fresh respiratory disease patients images.
And, uh, it was already labeled, so I was able to take it, put it in the tool at the annotations train a model out of those. And then.
You are, it's, it's, it's once once once model is sent back to box once it's made part of the same folder again.
This is all because of the, the nifty governance and access mechanisms that box enables.
We have access to that model here and when we are the next step in active learning is to train it to try it out on a on a different validation dataset and then use the learnings use.
The failures of the model provided a new, a new dataset. That has feedback from what it's a successes and failures work, and then create a new model.
And that is what this use case was able to prove that it is possible to do it in completely in browser as well.
And if you don't mind, just to make sure people get the specifics as well. So, auto email was used for the permit transaction and permit transition of the.
But the model is delivered in these examples with.
So, it's a JavaScript library that cause these components the browser is now a much more interesting machine than it used to be in the past. There is a compiled point in the browser that can be used for.
High performance, computing and browser scale, but so the model in the browser, the date is not coming to a server. The state has not been submitted to a server past the point where the model was identified.
Yeah, so as, as was traditionally the case where the data would have to be sent to where the model was hosted here, it is the other way around the model comes to where the data is. And because the data is governed.
I mean, there is a little to no chance of of any data being compromised or lead or, I mean, any privacy concerns are essentially.
So, yeah, I mean, you can see it's quite fast as well. Um, can I now move to and yeah, this is probably the most interesting part of this application.
The fact that we are able to use, um, Eric and Joanne is a server a concept, have it hosted somewhere and then use.
A whole slide imaging data inbox and then display it and do stuff with it in the browser completely.
So if I zoom in and out, you can see the, the, the I think it'll have to reload because this is a sort of a limitation. This is a security limitation by box to make sure that the image is not too stale.
That governance is maintained, even if I come back after an hour. So, just, uh, it'll reload, but you can see the, the squares being loaded. These are these are the individual tiles that the types of returns But despite the fact that the the data is in box, the data is governed.
I'm able to use the tiles through boxes api's, which is, I mean, for us, which is an imaging archive system of of a guide and through that interaction, I'm able to use these styles.
And also make it so that those styles become my, uh, my data observation. So, if I mean, I can, I can make annotations similarly that I, as I was making before.
And I can make predictions, I can run the model on these individual dials. I mean, this image is 300 megabytes. So it's, it would be tasked to run it as as a whole in the browser.
But because I have titles, I have individual tiles, I can use those and send those to the model. That is again running in my computer to analyze. So if I, if I run the more, you can see.
So this is the magnification that the model is looking at right now these, these advertisers analyzing.
Once it's done that, once it's done analyzing ties of this size, it will move on to a, and a higher magnification. It will move on to.
To make sure people don't miss the mechanics of it. Well, this works to open your their tools.
Keep piling share it. So, what you see is that the you earlier going to get.
Will be the referenceable for you for a while?
But anything it won't anymore, because the, that's the.
The last 2 as the expiration time, so pass that time, you won't be able to use it anymore.
All right, so it should be able to see. Yeah so it's, it's it's a longer U. R. L. because, uh, it's, it's from box. This is not an open a public image. The, uh, the 1 I sent you before was.
And hopefully having the same problems that.
And I think country was highlighting is if the link has to be occupied. So you can just click on it. Webex. There's something funny to the links in the chat box.
Um, I, I can show it to you on my, on my browser.
Huh.
I'm not sure what I'm doing wrong. Right? Oh, I think it is. I think it's because a lot of people are trying it, trying it out at the same time. So the tide server is hosted in a, in a service instance.
So, I think it might be that it is overwhelmed with the load. That is, that is probably I doubt it. My, it's something to do with Webex.
I think it's just that too many people are trying it out in the same time. Yeah, because this is this is not an, this is.
No, this is this is not a product.
Sorry, that's a great segue for the Google contract. Excellent. Yes, exactly. Yes.
So, because this application isn't, uh, isn't exactly in production.
We have to make those,
uh,
I mean,
we're fine with having a service service instance of the server,
but of course,
at scale,
it would be that there might be better options that we look at.
Um, I, I think we, we stop here for people to ask any questions they ask about the demo and we go back to the.
Architecture after that. Yeah, sure.
You want any questions.
I have a question on.
Her formation something about privacy um.
Can you clarify whether the data the act that's access through this?
Service needs to be the identified or not.
So, I mean, it depends on your use case, I would say, okay, so if you don't mind.
Yes, it is fun because understand kevin's question. So so, Kevin, only to the extent that you need to have them, wherever you keep them.
Because after the model is trained, you're right running it on the place you have access to.
You know, the data is not moving anywhere else.
So, if you need to identify them, you do it in the place very start. If you are in a clinical setting where the application may be important. For some reason, you could be using the exact same tool to the same server.
To do that, because the image is never leaving the place where it is, of course phasing piles that.
On the identifiability components, something scribbled on the image, noticed that information is coming only to your browser. It's not going anywhere else.
So, is this only for running a Pre train model, or what about for training the model?
So, the modeling this case was to auto email.
So you're right, this is already trained. No, it's not. What properly starting to hint here is that this is also the ideal platform for Federated learning where the learning would not happen on the service side.
So, as a Google service, and it will be distributed among multiple repositories and maybe this is the most exciting use of this too is that because they're distributing the permit transition algorithm.
That means that the code will be moving across different repositories and the parameter values would be changing in response to that engagement. So, again, the data would never leave the place.
Now, in that case, security of data isn't issue because of the coefficients are shared. And those, I assume, are.
Publicly accessible.
I didn't understand the question if the weights are publicly shared, just possible to reconstruct the data theoretically, unless there's some security mechanism in place.
No, that's right. It is theoretically possible. So that's the place we are. What is theoretically possible what is practically objectionable.
Right, but that there's, there's, there's that sort of part and parcel of the whole broad issue.
Of federated model training and obviously it's an important issue and it doesn't go away. Obviously here.
Yep.
But the question we are granular, could that intersection.
Or or that inference be, would it be granular enough to identify patients or not? So, I think those are important issues when we are deciding what sorts of topologies.
We are writing, but that's that's exactly the sort of constraints and preoccupation that drive the identification of the model.
But not experiment translation, so the has to satisfy their concern right? But I think you also have.
Issues quite a lot of ish number of issues with.
Proprietary Ness and data material transfer agreements where.
The data actually does not have.
But there's there are a lot of issues involved and distributing it any way. And in that case model waits.
I think many people would believe.
Are far enough abstracted from the data.
So is to make these things, um, something you don't have to worry as much about.
Yeah, that's exactly what we see. So perfect. If you don't mind, I'm going to share trying to share my screen. Now you I think you need to stop sharing yours.
I just have 1 question for clarification.
The results that the model produces, they stored on your local computer.
So that is again something that a box gives us, they are attached to the file in a way.
So, um, in the in the case of where we have, I mean, where, where we don't expect as many annotations, we use boxes metadata service to store it as close to the file as possible.
So, um, it's, it's, in essence, sticking to the spirit and, uh, in the case of full start imaging, the predictions are stored in a in a separate file, but they are stored under the same governance.
So, they're they're stored in the same folder in box basically for anyone who has access to that. Folder can also access the predictions. So, that's that's how we think about giving authorized access to people. All you would need to do is share that folder with someone else.
And they will automatically have access to the model.
The annotation definitions that you created the images and the predictions.
It's not local. It it is, it is stored back to back to the cloud.
I think some links, thank you Congress, Congress session and wrong.
Where where the indexes are they circulated all their adjustment.
Not be too technical about the process.
Is distributed we also started playing with staples dissent algorithm, so distributing them between different machines.
And the results confirm what you read in the literature is that there's actually not much of a break in in performance not as much as as we feared. So, I have a slide here trying to summarize the point we've been discussing.
So, we have an API, so the server is really what triggers suddenly all of these things and this API is now inserted in between the data.
And the analysis, so this includes a visualization and so that was the 1st demo showing that.
Once we move to the API level, we can see the tiles and the tasks can be sent to different places. So I appear snapshot. Just like the 3 use cases.
1 is from Jupiter notebooks I guess you can barely see the solution.
This is example where you're running trained models.
On a whole slide image and you have this open promise that's the edge we are walking now.
Of trying to do the distribution of the permit transition.
Among machines, rather than relying on the back end to do this to say the research level I don't anticipate.
Uh, useful to in the short term by any means, but the, the proof of principle seems to be there and, of course, for smaller images, like those of Radiology or.
The, the tool is immediately use useful and it is used can see at a disadvantage. For instance, we have a collaborator from Canada.
When she returned to her home pathology department, she wanted to use the tool to pacify her.
And again, she didn't see any discontinuity. The center would work in images. She could not share.
With us, so hopefully.
The role of the API is not is not clear. So, the key is here.
Represent the scope of the token, the token identifies the user. So each token is issue and the scope.
So, things you can do with the image is images you can see or not see.
And those are representative with the key. So this is an attempt to.
Uh, summarize the presentation before we move to the specifics. So now we have the link if you walk into ask questions where we come back to this.
Diagram or the previous 1 just want to go through the 7 points.
So, the 1st, 1, is that range request allow us to deal with very large files the same way. We deal with small files.
So, the small versus big issue disappeared completely. The tile is a camera. If you want.
The governors, if it's level.
We avoid all these issues of having silos and then having to move the count.
To the inside of the silo where the control of the data access so the distributed reposition images will no longer be under.
The purview of the person submitting the images. So there is an element of trust is established.
Once people we work with realize that they can very easily exclude us from seeing the images.
Where they are using our code to analyze them. So this fact here means that you can all start doing a distribution of.
Building on the existing preexisting trust.
By the fact that the images themselves, the data itself is controlled by those who submitted.
So, the privacy concerns, I mean, the writing I said, without I realize, I should have said diminished when the argument is the 1 Joe was highlighting that there's a level of obstruction with the right. That's.
It's also a level of protection we can't easily for pixel values from the.
Values and also this point that is very important, especially when collaborators move to places where they don't have say, a division of pathology within a department of pathology. So they wouldn't have pathology support.
And also, they don't want to be settled with costs that they would have a hard time anticipating. So, if you can distribute to models.
To the browser directly using web assembly as the execution engine or even something less powerful than that. But that's the browser is advancing in the same direction.
These architectures are so we are looking at distribution with no nominal costs.
So, the real costs are the electrical Bill of the person we wear the browser. So as a computer where the browser is running.
Number 4, for me, it's a little bit the realization that the goals we are trying to take out, it's really the pathologists annotating images.
But we don't make it too easy for them to do this specially not in distributed groups where different people will have different knowledges and where they may want to disagree about the classifications.
All of this is useful information for the active learning approach and this level of the Catlin allows us to distribute these tools entirely web applications. Number 6 is.
If it sounds like we are breaking it, something we do in the intermural, but we don't like people in the.
The actual program used business, the exact opposite we fought for this professional services contract with Google. We have to thank the strides initiative at an edge to do this. So really kudos. 1st strides.
We couldn't work without them.
So, thanks to this mechanism, they are now hardening the server and you see it pop up.
In the GCP marketplace where you can then start it associated with your billing account. The cost of the cloud server are, of course, a small fraction. If any alternative.
Where you'd have the code deployed where the date is and notice when I say this and the necessarily mean in your physical infrastructure.
It could be, for example, in a containerized infrastructure, so the continuous infrastructure would be for practical purposes. The, the local 1, the costs are much lower when we use what's called service approach, which is what.
The service provides.
The last 1 is 1 that worries me a lot. So I often I'm in a conversation where it sounds like we're talking about the, the comments.
But then, when I ask for a mechanism to the reference, the power from an image, or or anything else, I'm told that I have to be inside the incline. So, this conversation, if.
The systems are truly the realization of the data comments proposal by Bob.
And in that case, that enclaves architecturally are on the client side.
And the data comments, not at its core. So this is very much recession. I'd like to promote because it makes a huge difference for us.
Being able to distribute things where you can keep the.
Calls to accounts server that you can own versus telling you to adopt 1 of the enclave environments. When I say you, I'm the user community.
Forcing people to move away from the analytical environments we often hear from it. We also send by statisticians that any solution that will move them away from says, or our studio is the no, no.
So, we don't these api's that is also out it's not just the distribution model with the applications.
Is also the distribution model where the researchers remain in the analytical environments of their choice? And I think I just finished the last point and session is open for.
And the discussion comments, questions.
I think spirits has a question, please unmute yourself.
Yes, thank you. Hi. Hi, Johnny. Sorry. I was only able to join half halfway through the call for your presentation and it was was surprised to see that exciting the, or at least showing if figure for my paper don't send me something.
Yes.
That was quite nice. So point 7 when we talk about data and claims, can you just explain that a little bit? You might have explained it in the beginning, but I missed it. Sorry if that's the case. Um.
And I'm particularly interested into that, because we are working on the.
On on, on computational and claims on our end and most specifically on trusted the execution environment. So I'm just trying to understand.
What do you mean by data enclaves? In claim as a number of symptoms for the user? The symptom is that the user is told to move to another environments.
Not 1, they usually working for and I try to capture this in this diagram here.
So, for instance, if you are in the environment that tells you, you have to use Jupiter notebooks as an example.
And so you can do it and you are happy with it if that is your choice of analytical environment what if.
Your choice environment is something else faces is our studio or is a web application, because you're engaging the image directly in the image.
You you mentioned, so, in that case, we are moving people away from the analytical environment in 2000.
That is not where they were born, so to speak. So the fact that you have analytical enclaves. That's excellent. So, my question here is that.
From or I guess that would be your question from your analytical enclave. Are you able to call the images.
Of the people that you're trying to serve with your analysis.
And if the answer is, no, you need to go somewhere else and there is no way to call these images.
Then these images will be an inquiry if the answer is yes, then you're just using other departments. That's exactly what people to do is to use that equipment.
From whatever analytical environment or distribution mechanism.
I see yes, yes, yes yes it did make sense. Thank you.
Is there a limitation to the vendor specific file format that you support.
It's anything by reformats supports, and I think is the right person to answer to this 1 I'm not sure if he's with us.
So we've tried multiple formats as you could see in the list of essentially what bio format support. But there are some files, the files that are actually, like, 1 or more files, which has to be dealt with a little differently. Because there are like, multiple files represent 1 image.
But things like big TIFF.
Uh, and these are all supported thanks to all hard work by, on biochromatics.
And I'm very much interested in adding the, um.
Dot com, dual personality file format capability to it as well.
Okay, thank you. I find you within the.
So, I believe that it's coming through, just like anything else. The same calls.
And this is using the little server that's you guys just crashed.
So, I guess, I guess it's all working. Well, again.
And so I was a little confused about your terminology use term range requests, but in your paper, you talk about using the I.
F protocol, which is different than traditional range requests. So, can you explain the difference between those how those to fit together?
Yeah, see, you again to what they're saying, the same.
Range request by way of requesting a sub allocation in a file on I. F, is more of a protocol.
For accessing images, it has a lot of similarities to dotcom web, uh, another type of interface I would like to support in the image box. 2 server.
Um, but they aren't quite the same.
1, is that you are all protocol? The other 1 is an underlying protocol.
Yeah, this is a generic mechanism can be used to anything.
Thanks to which farm it's we know where it comes. Uh, David, I had had to put you on mute to make the echo go away. So please unmute yourself. If you have follow up questions.
So, you see him an example where instead of images we are looking at large files in the multi gigabytes size and it's the same thing. You can specify the bite range.
And you get what you need, could be here an example. So, this is the typical policy, the, the metadata, but we can go somewhere.
So, what I'm trying to illustrate here is that it doesn't matter what the file format is it should be range request is a web server.
Vacation so jumping right in the middle, you know, I have something else.
The size of this image is 16 gigabytes.
So, this helps you cannot things about the file without downloading it at all faces. The size is 1 of them wrench requests, allow you to get the specific slides of the image.
Got it and sorry about the echo. If there is an echo just have 1 more related question and where do you get the index for the relevant range request that matches the particular tile that you want and and how do you communicate that?
Or, do you depend upon whatever product you file format that index is stored in and your server side analysis of that?
You know, that's a great great question. So that's exactly what does.
It allows us to know once, you know, what is the position and the rest of the scale.
Allows us to know exactly where that image is in the, in the image pyramid.
So, we don't my performance for me in trouble, Eric, something.
Yeah, that's correct. I mean, yeah, I understand Thank you.
All right. Okay then we promised to stay within 45 minutes. 4 minutes past that. Mark. It's your fine Thank you.
This is a great presentation other questions for Jonas for work.
Okay, and I want to thank the speakers.
Thank you and the slides, I think would be shared.
To our wiki page is that correct? Yep, I already shared the link, so okay. Well, thank you.
I noticed we'll continue with the.
Updates and.
Justin on or John? Yeah, I'm here. Okay.
Okay, so updates from the cancer imaging are crap for the past month. The 1st, 1, is that Frederick National lab was soliciting proposals for.
People that were interested in labeling cancer imaging archive data sets to make them more readily usable by data scientists for artificial intelligence. The solicitations open until.
Into this week, I think Friday, February 1st, so take a look at that. If you're interested if you could put it in the chat, that would be that would be great.
Yeah, we can do that and then also, if you go to the cancer image archive dot net, and just click on news, all of these things are available that way as well. But I'll post that as soon as I'm done in the chat.
Then the next 1 was highlighting a nature scientific data paper that came out about the code 19 data set that was published by the team that, uh, University of Arkansas that does all the curation work for tca.
Um, then we had a data set update for the large scale and pet data set for lung cancer diagnosis.
The investigator provided us with some corrected annotation files and, uh, associated supporting clinical data.
We also published the presentations that we did it in a hands on workshop. These are video tutorials that will walk you through a bunch of the basic functionality of cancer imaging archive. So if you.
The link will take you to our user guide page and all the videos are embedded right here. It's about 45 minutes worth of content to go through.
Uh, and then we had a new analysis results status set published. These are, uh.
Forecast segmentations of images from the prosthetics challenge.
As well, as a reminder about the data set, that same group had published previously from the same collection, looking at segmentations of the entire prostate gland.
And then just today we're highlighting a.
A project that was using, um, data sets for creating teaching files.
Um, so there's a little video here showing the user interface on this thing. If anyone's interested to go take a look at that.
That's it. Thanks. Thank you, Justin. We'll continue with IDC updates.
Can I ask a question about the CIA? If do we have time? Yes, please. Go ahead.
Thanksgiving, so that's a question, not specifically for Justin, but I guess that perhaps is the best Avenue, the best venue to ask these questions.
So,
there is has been instrumental in various studies and it has a lot of data sets available,
very rates 1,
some of them specifically that they span across radiology,
pathology,
molecular,
and multi,
all mixed,
not even genomic information,
but much more than that.
Quits render themselves as as very, very useful data to be used on on international efforts, for example, computational challenges and that's just a single example.
So if we.
Are considering multiple of these data sets to be included. There is some quite a bit of effort of curation that still needs to happen on the data set and that but that's fine.
That's you know, whoever wants to organize something utilizing data should go perhaps through that curation process. It's.
Though, what is extremely cumbersome, but sometimes is finding specific information that.
Might end up being might end up exist within the data set, but you really need to share it throughout every single whole thread. Every single file that exists in every single portal. That makes available all these derivatives data types.
If you want to certain data collection,
and that was easier before Wayne was and related collections but now that the last few years that the city has expanded way beyond that,
this project is gets a little bit more cumbersome.
So, is there a particular way.
Or a particular person that these such requests should be addressed or should we just, you know, main bombing you just in that? At least this is what we've been doing so far and sorry about that.
So, what is what is perhaps.
Away and if the, perhaps the question should be, is there a way.
To obtain certain pieces of information of specific data sets.
That exists in.
And if there is such a way, what is that way or what is the person that should indicate that way.
Yeah, so if I'm going to read into your question, a little bit based on some of the emails, we've exchanged lately. So, for example, feel free to give an example. Yes. Yeah. So, with with tech, I, I think part of the struggle that you all have expressed is, you know, you have.
Imaging data, that's in the cancer imaging archive and then you have genomic data and the genomic data comments and proteomics data, and the proteomics data Commons. And so you have this.
Number of different websites, where you're chasing down different pieces of information for the same project, and they're all linked up by patient ID, but it's still kind of cumbersome trying to figure out. Where do you go for what piece of information?
And they all have different user interfaces and and so I,
I think part of what you're asking is,
is there are there any plans to provide a more project centric,
integrated portal for these kinds of things to make it easier to find all of the data for a,
given set of patients set a fair.
It's it's which 1 of the 2? Yes. That's a fair. That's a very, very good approximation of 1 of the 2 questions. I guess. The more and more important question is how to gauge what.
Exact information now there is there or is there if or a specific information exists not to find that perfect information.
To find, for example, if we're looking for a particular mutation, molecular mutation do not asking for obtaining that data, but get collected summary statistics. How many of these patients do they have this data?
So then, we can decide, make decisions of whether we can incorporate these data sets to our analysis, our challenges, or whatever the task is.
So, once we decide which status is the use case, we and everyone that is interested and go through the pain of curating the data. But.
Curating on the data, just decide if you're going to use it.
That's a little bit, you know, it's cumbersome and it doesn't even conclude on something. So that kind of initial step.
Of that does this information exist?
Is there is there a mechanism that we can tackle.
We configure based.
Yeah, I think.
Then he came on it.
What we're trying to.
Tci is primarily to archive.
It's not really.
I don't think that you're able to share exactly where you're just going to say. Well, briefly, that question came on and really? Because it's really.
An issue that's being addressed by the engine gay Thomas, the cancer research data comments.
Does its best to be able to to search related data?
We just don't have the scope to create a full metadata search interface that it is being developed.
I have a.
Yeah, I can thanks John.
What do you mean? Yeah. Uh, thanks. Spirit for that question. So that's a kind of service that the cancer research data comments is expected to provide.
It does not currently do that, but, uh, I think looking at in the, into the future, maybe by next year, we'll start to have some features that that could.
Point to that access to data for same patient cohort across different data elements and cancer chunks Proteomics, et cetera. Yeah. Hi, Kate.
This is David pot speaking cancer data aggregator cancer data aggregator started with proof proteomics and genomics. Some of the data sets.
Next.
Data resource is going to be the imaging data Commons now imaging, data Commons.
Only takes the data from so, um.
It is a end goal for us. Are we there? Immediately? No.
If you have an opportunity we're presenting to tomorrow to describe the complexities of the.
They didn't consistencies and such, so, um.
Yes, it's an angle and.
Very much to understand the goal. Thank you the.
So, at the moment there, the, the best way to go about it is.
Mail bombing testing and then folding indications that, uh, that he pinpoint.
Well, technically, I'm going to pull myself out from under the bus here and say that you should contact the help desk and they'll escalate when they don't know the answer to these types of questions.
They can escalate it to other folks on our team to help answer them for you. But and I should also point out that.
I think that there are help desks for some of these other resources, the genomic data comments, and the proteomics data comments and so, 1, other thing that we're trying to do is in the user interface, make it more clear.
When you're looking at data that really we're not hosting and we're just.
In providing you access to another resource that happens to have data from the same patients. So that it's more obvious that, you know, whether a question is something that's actually for. Or if it's a question that you should be asking the genomic data comments team, or some other resource.
Thank you that's that's yeah, that clarifies quite a bit. That makes us a little bit better, you know, so we don't just.
I'm sending you a bunch of emails, uh, about things that are probably unrelated to your tasks. Thank you so much.
Well, thanks for that question, as far as any other questions were just.
Okay, not only can give the.
It's just a brief update today the IDC team is heavily working on developing the software that hosts the images.
They are going through agile development cycle so you will see quite frequent updates to the user interface. So, please come and check regularly and provide feedback to the team.
If you have ideas, if you have suggestions, and there's the email that you can use 1 of the changes. I would like to highlight that came out recently.
Is a much improved version of how to export cohort manifests. So, this is basically a list of the images that you have selected as your particular cohort, and you are interested in.
You have 2 options you can download the.
Description of the files you have selected, and then hand them over to the cloud resources.
Or you can export your data into the query tables and then do a Pre, a quick analysis on them.
I also would like to highlight that.
The team, the NBA team, and the imaging data comments team are working closely together.
To establish work flows and procedures to make data flow from tca to the imaging data comments and I very much enjoy.
The initiative that all 3 teams spring.
To the task, I also wanted to remind you that please don't forget the imaging data comments as offering free cloud, Google Cloud credit. So you can try out the platform.
And again, please let us know if you have any suggestions.
For improvements and any ideas for the future.
Thank you Lee, I would also add that your the imaging that is very interested in your.
Suggestions for use cases are to help better familiarize.
Users in the cloud computing environment.
So, we welcome that no suggestions as well.
Okay, so this is it for our February.
Meeting I just wanted to make an announcement that beginning next month, beginning with the March. Um, webinar.
We'll be working more closely with our colleagues in the cash program.
In particular Dr, Robert Nordstrom, uh, who.
Many of, you know, and this would help us to offer wider breadth of topics, uh, in imaging and imaging for medics.
I believe that they're going to be inviting the next speaker for March and.
I think Dr Nordstrom is on the call. Do we have a speaker yet or? We announce that later I'm working on the speaker as we speak so okay. We'll have somebody from this group.
Yeah. Okay. Thank you. Bob look forward to working together.
Okay with that I'd like to thank everybody, especially the speakers for great presentation and great discussion and for all of you for attending, thank you so much.
Have a nice dress of your day. Thank you. Bye. Bye.
Thank you. Thank you. Bye.
Silence.