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2018 GTC Washington DC

DC8150 - How Can Synthetic Data Revolutionize AI for Healthcare?

Session Speakers
Session Description

We demonstrate that labeled synthetic medical images can be used to train deep networks for accurate cancer diagnostics, specifically in applications where annotated data is limited due to privacy concerns, underrepresentation of rare conditions, limited availability of experts, etc. Deep networks trained on limited data also suffer from the cross patient network adaptability problem where networks trained on one patient often fail to generalize to other patients. We show that by using synthetically generated medical images, we can train accurate deep networks for cancer diagnostics in two different medical imaging applications: a) polyp detection and classification in endoscopy for colorectal cancer detection b) breast cancer grading of histopathology images.


Additional Information
AI in Healthcare
AI in Healthcare, Deep Learning and AI
Healthcare & Life Sciences, Higher Education / Research
Intermediate technical
Talk
50 minutes
Session Schedule