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2017 GTC San Jose

S7317 - Improving Network Accuracy With Augmented Imagery Training Data

Session Speakers
Session Description

One of the biggest challenges in machine learning today is producing the training data. We'll compare different methods for augmenting a medical imagery training dataset for supervised learning. The different augmentation methods are assessed with respect to their impact on cost, network accuracy, and overfitting. We'll focus on prostate cancer data from the Joint Pathology Center, which is being used in the White House Cancer Moonshot project.


Additional Session Information
Intermediate
Talk
Deep Learning and AI Federal Healthcare and Life Sciences Medical Imaging
Healthcare & Life Sciences
25 minutes
Session Schedule