|Authors||A. Storås, I. Strümke, M. Riegler and P. Halvorsen|
|Title||Explainability methods for machine learning systems for multimodal medical datasets: research proposal|
|Project(s)||Department of Holistic Systems|
|Publication Type||Proceedings, refereed|
|Year of Publication||2022|
|Conference Name||ACM Multimedia Systems (MMSys) Conference|
This paper contains the research proposal of Andrea M. Storås that was presented at the MMSys 2022 doctoral symposium. Machine learning models have the ability to solve medical tasks with a high level of performance, e.g., classifying medical videos and detecting anomalies using different sources of data. However, many of these models are highly complex and difficult to understand. Lack of interpretability can limit the use of machine learning systems in the medical domain. Explainable artificial intelligence provides explanations regarding the models and their predictions. In this PhD project, we develop machine learning models for automatic analysis of medical data and explain the results using established techniques from the field of explainable artificial intelligence. Current research indicate that there are still open issues to be solved in order for end users to understand multimedia systems powered by machine learning. Consequently, new explanation techniques will also be developed. Different types of medical data are applied in order to investigate the generalizability of the methods.