Carles Garcia-Cabrera

Cardiac Magnetic Resonance Imaging is one of the most widely used scanning methods for acquiring data from patients for a variety of medical conditions. Similarly, electrocardiograms provide much information about different parts of the heart and its cycle. Whilst adoption of machine learning techniques in medical image processing applications has been slower than in other domains, this is a growing area given the potential of machine learning to assist in diagnosis and reduce costs. There are already examples of machine learning algorithms using such sources individually for identifying and locating issues. Using both signals at the same time is an interesting research direction as it could lead to performance improvements while enhancing the explainability of the diagnosis that this kind of algorithms usually lacks. Data is a key challenge when trying to use off-the-shelf algorithms in this area, specifically the amount of annotated data and its quality. Many researchers report in the literature how they struggle to achieve good results with existing annotated data, especially when working with open datasets. Furthermore, in some cases, there is much data annotated but with noisy labels that lead to very poor accuracy outside the training datasets. For this reason, what I would like to do in my PhD project is to address this challenge by using semi-supervised learning, not just to overcome the lack of labelled data but also to try to improve the performance over test sets and to enhance the explainability of the algorithm. To achieve this goal I will use data provided by collaborators in Tampere and perhaps some of the available open datasets. With it, my goal will be to bring to this field some of the current state-of-the-art techniques in computer vision for similar problems and look to extend them based on the learnings obtained. I strongly believe that succeeding in my objective will have impact in the medical and health sciences field, enhancing and cheapening the diagnosis, and improving the current explainability of state of the art machine learning algorithms.