Silicon Valley May 8-11, 2017

S7420 - Deep Learning of Cancer Images for Precision Medicine

Olivier Gevaert ( Assistant Professor, Stanford University )
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 Ph.D. 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.
We'll 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 CT images and clinical parameters. We'll evaluate our framework from three independent cohorts with survival data, and show how the addition of clinical data improves performance. Furthermore, we'll describe how image 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.

Session Level: All
Session Type: Talk
Tags: Healthcare and Life Sciences; Deep Learning and AI; Video and Image Processing; Medical Imaging

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