OHB-4306 Deep learning in biomedicine: challenges and opportunities | Devoxx US

Deep learning in biomedicine: challenges and opportunities


bigdata Big Data

Grand Ballroom 220A

Thursday from 9:00 AM til 9:25 AM

Massive amounts of data are being generated in biomedicine ranging from molecular, cellular to tissue scale data. It is now possible to study a patient from multiple angles and scales with multi-modal, multi-scale, high dimensional and high throughput biomedical data. However, these data are noisy, sometimes parts are missing, and it is unknown what entities are important for “precision medicine”, the concept that medical care has to be designed to optimize treatment of a patient by using the patient’s own data. In parallel, deep learning has revolutionized fields such as image recognition, natural language processing and, more broadly machine learning and AI. Now, deep learning is becoming increasingly more popular for analyzing biomedical data. I will introduce the type of data in biomedicine ranging from images, text to numeric data, and the potential opportunities and challenges for modeling this data using deep learning.

Olivier Gevaert Olivier Gevaert

Olivier Gevaert is an assistant professor at Stanford University focusing on developing machine-learning methods for biomedical decision support from multi-scale biomedical data. He is an electrical engineer by training with additional training in artificial intelligence, and a PhD in bioinformatics at the University of Leuven, Belgium. He continued his work as a postdoc in radiology at Stanford and started his lab at the Department of Medicine, Biomedical Informatics. His lab focuses on multi-scale biomedical data fusion primarily in oncology and neuroscience. The lab draws methods machine learning methods such as Bayesian methods, kernel methods and regularized regression to integrate molecular data or omics such as (epi)genomics, transcr