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2017 GTC San Jose

L7145 - Image Classification with TensorFlow: Radiomics - 1p19q Chromosome Status Classification using Deep Learning (Presented by NVIDIA Deep Learning Institute)

Session Speakers
Session Description

Thanks to work being performed at Mayo Clinic, approaches using deep learning techniques to detect Radiomics from MRI imaging can lead to more effective treatments and yield better health outcomes for patients with brain tumors. Radiogenomics, specifically Imaging Genomics, refers to the correlation between cancer imaging features and gene expression. Imaging Genomics (Radiomics) can be used to create biomarkers that identify the genomics of a disease without the use of an invasive biopsy. The focus of this lab is detection of the 1p19q co-deletion biomarker using deep learning - specifically convolutional neural networks ? using Keras and TensorFlow. What is remarkable about this research and lab is the novelty and promising results of utilizing deep learning to predict Radiomics. Prerequisites: Basic understanding of convolutional neural networks and genomics. This lab utilizes GPU resources in the cloud, you are required to bring your own laptop.


Additional Session Information
Intermediate
Instructor-Led Lab
AI in Healthcare Summit Deep Learning and AI Healthcare and Life Sciences
2 hours
Session Schedule