20.500.12556/RUL-143700 Explanations of medical prediction models using background knowledge Razlaga napovednih modelov v medicini z uporabo predznanja Prediction models are very useful in many areas, as they provide decisions as well as an understanding of the problem. In medicine, they are often used to predict diseases, outbreaks, reactions to medications, etc. Data scientists are striving to improve these models to get more accurate results as well as a better understanding of different phenomena. Since deep learning models are considered black boxes, the output decisions are not easily explained, but their interpretation would be very beneficial. In this thesis, two different approaches to medical model interpretation are shown. The first explains with contextual decomposition, focusing not only on the importance of singular features but also on interactions between them. This way, we can understand complex features and their role in models. The second approach leverages saliency maps in order to provide visual explanations through parts of images most impactful in the prediction model. A comparison of both methods on a skin cancer dataset shows similarities and differences between the two. The results show that the second approach gives us more understandable explanations, while the first one is more useful when trying to improve models' accuracy. Napovedni modeli so uporabni na mnogih področjih, saj zagotavljajo odločitve in prispevajo k razumevanju problema. V medicini se pogosto uporabljajo za napovedovanje bolezni, izbruhov nalezljivih bolezni, reakcij na zdravila itd. Raziskovalci si prizadevajo izboljšati modele, da bi dobili boljše napovedi in boljše razumevanje različnih fenomenov. Ker modeli globokega učenja veljajo za črne škatle, njihovih odločitev ni enostavno interpretirati. Izboljšave na tem področju bi bile zelo dobrodošle. V diplomskem delu sta prikazana dva pristopa k interpretaciji modelov. Prvi uporablja kontekstualno dekompozicijo, metodo, ki se ne osredotoča le na pomembnost posameznih atributov, ampak tudi na interakcije med njimi. S pristopom lahko razumemo kompleksne značilke in njihovo vlogo v modelih. Drugi pristop izkorišča pomembne značilke, da najde vizualne razlage, kateri deli slike so najbolj vplivni v modelu. Primerjava obeh metod na problemu kožnega raka pokaže podobnosti in razlike med obema. Rezultati kažejo, da nam drugi pristop daje bolj razumljive razlage, prvi pristop pa je bolj uporaben, ko poskušamo izboljšati natančnost modelov. artificial intelligence predictions background knowledge medicine explainable AI umetna inteligenca napovedni modeli predznanje medicina razložljiva umetna inteligenca true false false Angleški jezik Slovenski jezik Diplomsko delo/naloga 2023-01-09 13:25:00 2023-01-09 13:25:08 2023-01-12 05:25:40 0000-00-00 00:00:00 2023 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 34906 137274883 Lumburovska_Leonida_-_Razlaga_napovednih_modelov_v_medicini_z_uporabo_predznanja.pdf Lumburovska_Leonida_-_Razlaga_napovednih_modelov_v_medicini_z_uporabo_predznanja.pdf 1 4C583288E264251881E70635332E6D5D 370b9250aa61b7d13c26867f96b7a3444ed6ecf4f28fc96d1d8ebad5b2d8a546 6d24f4a6-9018-11ed-9c94-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=165569 Fakulteta za računalništvo in informatiko 0 0 0