AI Generated Video Summary
So my background is not only scientific, I also have founded in 2010 so 12 years ago, a company, a service company working for fortune 500 companies, building also data science, machine learning solutions. And yeah, before that I had I did also some some, you know, some other commercial work, for example, at IBM. So, as I said, I have 15 years of experience in machine learning and specifically in medical imaging, I mean, in applications in medical imaging.
So, how, why I why I decided to actually cover this topic and to build some solutions in this area? Well, as you can see, I don't I'm not really in the risk group when it comes to skin cancer because, you know, the biggest group of of the risk group is actually the blond people with blue eyes. So, this is the phototype number one with the highest risk of having skin cancer, especially if you're becoming kind of your skin doesn't doesn't isn't well it doesn't become brown when you're exposed to the sun but actually it's more going in the direction of red, and actually also, the risk of actually getting skin cancer is high in this group.
So, the darker the skin is and how it reacts to the sun, the lower the the probability is to get a skin cancer. So, there are six type phototypes of skin. I'm more or less in the third group because of my color, hair color, eye color, and so on. That's why the biggest problem actually, it is the biggest, the countries like Germany, Scandinavia, and the Nordic countries, the US, Australia, especially Australia, this is actually where this problem is even more and more important. In the meantime, I also have done some partnership with some dermatoscopy companies, I mean companies who actually develop the hardware. So yeah, as you can see here, here's one of the device. This is our dermatoscope here. That's something, that is a device that is actually used by the dermatologists. In this case, I have also used an iPhone here on the front because this is actually an extension. So it's not a typical dermatoscope, usually it doesn't come with an iPhone or any kind of mobile phone. It comes alone, it's a standard on the device. Some dermatologists use also this kind of extension case just to take the pictures in an easier way. And obviously it's quite small, so we can take it to your pocket and actually visit even your patient to take a look on the mole like this. So this is how actually my solution is used and it is combined with the special lenses, special light to get the best possible image of the skin mole. When comes to the data set because any kind of machine learning topic, model should be fed by some data. Now when I started my research I actually started with 50, 53 images or less. So as you can imagine, that's not a big enough data set to do any kind of research. So what I did is I met, I guess, almost every company in public or private that do anything with dermatology in the city where I live, in Krakow, in Poland. Most of them actually declined to collaborate and actually build some models.
It was in 2007, 2008. So the way how people thought about machine learning was totally different than actually compared to what's happening now. Actually, machine learning is AI became a buzzword and everyone wanted to do AI. In the past, I mean, 15 years ago when I said AI, most people said, oh, no, thank you. I'm not interested. Now it's totally opposite. I need to explain people why not to use machine learning rather than actually use machine learning. So it changed dramatically in the COVID pandemic, even increased the hype on AI.
So when I reached to the companies, I obtained a data set of around 5,000 images. Now you can easily download a data set of about 26,000 images of skin moles. It's available on the ISICarchive.com website, and you can use it for your research. So now it's even easier to develop algorithms to find different kind of skin illnesses, not only cancer, which is technically, I mean, it's not the most popular. That's good illness when it comes to skin.
5. Building Neural Networks and Analyzing Skin Cancer
Let's just move back to the slides. When it comes to the skin cancer, as you can see here, you have three images and only two of them actually are cancers. The one on the left and one on the right. Here on the left, you can see some patterns, I mean, the dots here and the stripes. Some vessels here visible that all of that is not very symmetric. The borders are quite smooth really. Here on the right, you have this kind of a pattern that is called the blue wave pattern. You can see kind of a white-blue colors here. It means that actually, well, it's going deep. I mean, the cancer is actually going deeper into the skin and actually trying to get to the vessels. That's bad for the patient, but that's also a pattern that actually tell us, oh, that's really bad. Here in the middle, you have an example of a suspicious care role, but not confirmed to be cancer because all of them actually are pathology confirmed or not. I mean, confirmed that they are cancer or not. So how do medical doctors do that? They use some kind of a scoring methods like ABCD, seven-point checklist, seven-point score, hunter's call, three-point checklist, and so on, and so on. So they have, they use some panels, like asymmetry, border sharpness, number of callers. I mean, in ABCD you have six calls that actually, it really counts, and they check how many calls there are. When you combine it together, I mean, you write down one by one how many different patterns are available, you can count easily more than 30 patterns that actually the doctor, the medicals can find out on the image, or just, you know, using the loop. Directly. What you can also do, and what I did in my research, is that I used different kinds of wavelengths of light to get not only what you see with the visible light, but also what's actually deeper in the skin using, for example, the infrared light. Actually, I used four different wavelengths of light in total, but actually here you can see one with the infrared, UV, to get the, to get the melanin, right? The vessels more visible, and that's how you can actually do a better research, because we have more details, more information. Oh, what you also do, you can also do some image processing, like binarization in this case, right, to find out, oh, this here border is not smooth, right? You can, for example, use fractal methods to analyze that.
6. Extracting Asymmetry in Skin Images