Exploring Riemannian Manifolds for Medical Image Classification
Abstract:
This Master’s project aims to explore the use of covariance descriptors for disease classification with medical
images. First, the MedMNIST toy dataset will be explored. Then, the student will work with an open-source
medical dataset, e.g. of 2D chest x-ray or 3D cardiac MR images
Maxime Di Folco
Research Scientist
My research interest is the study of the cardiac function via machine learning methods, in particular representation learning methods that aim to acquire low dimensional representation of high dimensional data. I have a strong interest in cardiac remodelling (adaptation of the heart to its environment or a disease), notably the study of the deformation and shape aspects.
Anna Reithmeir
Doctoral Researcher
My research interests include deep learning for image registration.