How can beautiful algorithmic findings be helpful in our everyday life? One of the answers to this question lies in the area of healthcare applications. Nowadays machine learning methods are becoming more and more useful in medicine. They are able not only to assist medical specialists in processing large amounts of data, but also to help in diagnostics and patient follow-up.
This course is devoted to the discussion of some interesting applications of machine learning methods to automatically analyse medical images and physiologic signals. Medical images acquired by means of special equipment represent internal structures of the human body and/or processes in it. The most modern technologies for acquisition of such images are magnetic resonance imaging (MRI) and computed tomography (CT). Physiologic signals usually refer to cardiologic time series such as electrocardiograms (ECG), but can also represent other physiological data, for example, stride intervals of human gait.
Several important problems will be highlighted along with successful solutions involving machine learning methods including examples both from the worldwide practice and the author’s own research. Description of the basic principles of the algorithms used will provide a good opprotunity to strengthen the knowledge acquired from the other courses of the school.