Deep Learning Models for Personalized Medicine
In recent years, deep learning has achieved remarkable results in fields such as: computer vision, speech recognition and natural language processing. This deep learning revolution is slowly reaching the challenging problems of the medical domain, opening the doors for personalized medicine. Medical domain is characterized by high variability of data including text, imaging, and genomic data. I will discuss recent advances in two domains: imaging and genomics. First, I will briefly introduce the medical imaging segmentation problem and the contributions that we made to the standard pipeline. Second, I will present the challenges posed by genomic data and potential solutions.
Adriana Romero is a post-doctoral researcher at Montreal Institute for Learning algorithms, advised by Prof. Yoshua Bengio. Her current research revolves around deep learning techniques to tackle medical data analysis challenges, addressing impactful problems for society by paving the road towards enabling widespread usage of personalized medicine. Adriana received her Ph.D. from the University of Barcelona in 2015 with a thesis on assisting the training of deep neural networks with applications to computer vision, advised by Dr. Carlo Gatta.