Speaker
Description
Deep learning has had a profound effect in all walks of life, not least in medical research. The impressive performance of these methods is partly due to the development of very efficient approaches to solve highly overparameterized neural networks. Conversely, this leads to the problem of trust and interpretability of these complex models, which are key aspects in the medical domain. In this talk, we will see several works performed in our lab focusing on explainability and uncertainty modelling for a variety of clinical applications. We will then delve into the interface between machine learning and quantum computing, and explore early works on the development of quantum machine learning approaches for healthcare. First, we will show some results on the establishment of quantum diffusion models for medical image analysis. Finally, we will link back to explainability, formulating a quantum annealing framework for explainable deep learning