WLQ-7821 Deep learning with tensorflow for multi-scale modeling of cancer patients | Devoxx US
bigdata Big Data

Grand Ballroom 220B

Thursday from 11:00 AM til 11:50 AM

We will demonstrate a deep learning framework to predict survival of lung cancer patients by using convolutional networks to learn high-dimensional representations of tumor phenotypes from multi-scale cancer data and clinical parameters. We evaluate our framework from three independent cohorts with survival data, and we show how the addition of clinical data improves performance. Furthermore, we describe how noise can improve the robustness of our model to delineation errors and introduce the concept of priming which helps improve performance when trained on one cohort and tested on another cohort.

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