A student project associated with the CIS Collaboration Grant on AI for medicine has been launched. The project aims to address an additional research topic and allows students to gain experience in research and development.
Master project proposal: Interpretable Deep Learning towards cardiovascular disease prediction
Cardiovascular disease (CVD) is the leading cause of death in most European countries and is responsible for more than one in three of all potential years of life lost. Myocardial ischemia and infarction are most often the result of obstructive coronary artery disease (CAD), and their early detection is of prime importance. This could be developed based on data such as coronary angiography (CA), which is an X-ray based imaging technique used to assess the coronary arteries. However, such prediction is a non-trivial task, as i) data is typically noisy and of small volume, and ii) CVDs typically result from the complex interplay of local and systemic factors ranging from cellular signaling to vascular wall histology and fluid hemodynamics. The goal of this project is to apply advanced machine learning techniques, and in particular deep learning, in order to detect culprit lesions from CA images, and eventually predict myocardial infarction. Incorporating domain specific constraints to existing learning algorithms might be needed.
 Yang et al., Deep learning segmentation of major vessels in X-ray coronary angiography, Nature Scientific Reports, 2019.
 Du et al., Automatic and multimodal analysis for coronary angiography: training and validation of a deep learning architecture, Eurointervention 2020.
Good knowledge of machine learning and deep learning architectures. Experience with one of deep learning libraries and in particular Pytorch is necessary.
Dr. Dorina Thanou