3MD3070: Deep Learning in Medical Imaging

Medical imaging technologies provide unparalleled means to study structure and function of the human body in vivo. Interpretation of medical images is difficult due to the need to take into account three-dimensional, time-varying information from multiple types of medical images. Artificial intelligence (AI) holds great promises for assisting in the interpretation and medical imaging is one of the areas where AI is expected to lead to the most important successes. In the past years, deep learning technologies have led to impressive advances in medical image processing and interpretation.

This course covers both theoretical and practical aspects of deep learning for medical imaging. It covers the main tasks involved in medical image analysis (classification, segmentation, registration, generative models…) for which state-of-the-art deep learning techniques are presented, alongside some more traditional image processing and machine learning approaches. Examples of different types of medical imaging applications (brain, cardiac…) will also be provided.

Learning objectives
The course aims to introduce students to the design of deep learning methodologies for analysis of medical images. We expect that by the end of the course, the students will:
• have knowledge of state-of-the-art deep learning techniques for medical imaging
• have a deeper understanding of deep learning methods, applicable not only to medical images but also other types of data
• know how to build and validate deep learning models for medical images