I'm going to give a short presentation on our last semester project, which was inspired of some of the things Henning talked about with fixing some of the implementation problems that is reducing a CNN model in the healthcare sector. Our case was when we used X-rays, images to predict pneumonia. So there are a lot of problems and different things you have to take an account when you are trying to implement. The two we wanted to tackle or try to see if we can do anything about was the black box problem, which is like I don't know what this is and why it's making these decisions, why I don't trust it and the legal barriers, which kind of like elevate this trust issue. There is because the legal thing right now is that AI can't be legally responsible for the decision it makes and when you're trying to implement a CNN AI working in conjunction with a doctor, even if the AI, like was the one making the decision, is still the doctor that has held legal responsible for that decision. So trust in what it's saying is important. God. Our idea was instead of just visualizing what the CNN model is doing, we wanted to make it possible for the doctor or the radiologist to discuss with the AI on the individual level, like case by case, what are you saying, little AI, why are you saying it, what do I feel, does it seem like what you're saying makes sense and therefore like building a better relationship, working relationship with the AI and also doing that in like the same framework as a radiologist or a doctor in that case. So like it's bad, they don't need to have an education in computer science but by their own medical field can like understand it in the same framework. So the prototype what we made, this is what we can generate from an image, there's a lot of things that we're going to a bit of detail before. These two pictures over here is what comes out. This is basically we give a model X-ray image, it's our own train model from CNN, we're using CNN architecture, architecture and it says it has pneumonia, 99%. So that's like its prediction. Then we use something called a GradCAM. I don't have the time to go into all of the details of this for GradCAM, it's basically if you remember when Henning was talking about the convolutional neural network, like they make these filters, this is like, you know, we ask, it's like asking the AI which part of this picture, which features did you think were most important for your decision and it just makes a bit red blob. Now this is a bit hard to interpret for a doctor, I would say because it's, this thing just says these pixels are more important than these pixels, what does that mean? So what we did was what we call a set DC are automatic zone division of GradCAMs. So by using the GradCAM and we like try to divide the long into zones and then giving them more color depending on where they're most red pixels. So in theory, when a radiologist, he looks at the X-ray, he says, oh, I think that zone is most important for my diagnostics of that picture. Then the AI says, I think this zone and then, you know, the radiologist can look at that zone and the zone he thinks and determine, do I agree with this? Does this make sense? And if it doesn't, he can just trust it on a case by case. If it does, then you know, everything is wonderful. We also made this automatic box plot, you see a right lung, right lung mid, left lung and it gives the exact number of things. And again, this is all generated. You can take any X-ray image and throw it into this thing and this is what will pop out. Okay. Here is some examples of other X-ray images popped in. And if you're wondering, how do we make this, is it us that has drawn this like weird thing around it? No, we're using, and that's the fun part, we're using the first layer of this CNN. And it's like, it's the first eyes this computer has. It's a bit hard to explain at a short time. But here that's the first thing the CNN looks at. Like if you wanted it to say, is this a cycle red or black? You first have to explain it what the cycle is. The fun thing with CNN is that it does that automatically. So the first convolutional layer says that's a cycle, then it goes deeper, deeper detail. So we use the first convolutional layer because we saw that it drawed like thing around the lung with the grab cam. So we use that to make this so this is the lung and then we cut into it to make these sections. And the theory is that when a dog that would like, he would take an X-ray, then they will come back. This, the model would say, I think it's that high percentage of being pneumonia. This is where I look roughly. And here is like a shown division of what zones I think are most important. So that was like the idea of trying to do it. And here is, and we then made a GUI and the program to making it more, you know, friendly to use. Yes. So yeah, you can see, and then you can throw in any X-ray image and it will make that. A fun thing also we figured out was we had two models, we made two models just for the testing. And the thing is you can technically take any model you have and use this framework with it. If it's a CNN, if it has to, like, it's only through you have it really tested. So like, you can take any model if it's a CNN and it has been trained on frontal lungs, a long X-ray. So like, if it scans, if it, and that's the theory, I think everything will break down when I try it once. But like, in theory, it could work. So yes, that was the idea to make it easier to build trust and have a better working relationship as a doctor with your AI. So yeah. Any questions? Could you say a few words about your cooperation with this PS? Yes. We have worked, okay, I'm going to go back here just to that complain the background. We have worked with a PhD student named Dana, I think I remember correctly, who helped us a lot about giving us an image into what the relationship is right now between radiologists and the development of these kinds of AI's to help in it. And she was the one who pointed us in the direction of, you know, trust is in a general way, a lot of people are ready to trust these things. But the problem are really the legal limitations, that they can't just put all the responsibility on the AI's. So therefore, we thought of like a kind of solution to fix that in some way. She helped us a lot with a lot of the insight into the field because I don't know shit about radiology before I started this at all. I'm studying to be a data scientist, right? So yeah. Any questions? If I have time.