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DTSTART;TZID=America/Chicago:20181111T164500
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UID:submissions.supercomputing.org_SC18_sess147_ws_cafcw113@linklings.com
SUMMARY:Toward a Pre-Cancer Image Atlas through Crowdsourcing and Machine 
 Learning
DESCRIPTION:Ashish Mahabal (California Institute of Technology); David Liu
 , Luca Cinquini, Daniel Crichton, Heather Kincaid, and Sean Kelly (Jet Pro
 pulsion Laboratory); Kristen Anton and Maureen Colbert (Dartmouth Medical 
 School); Christopher Amos (Baylor College of Medicine); Matthew Schabath (
 Moffitt Cancer Center); and Christos Patriotis and Sudhir Srivastava (Nati
 onal Cancer Institute)\n\nWe describe how crowdsourcing can be combined wi
 th advanced machine learning for early cancer detection. We demonstrate ou
 r system for lung cancer (using data from the National Lung cancer Screen 
 Trial), but in such a fashion that it can easily be replicated for other o
 rgans. Thus this becomes a step towards the pre-cancer image atlas, a focu
 s area of the National Cancer Institute. Additionally, by keeping explaina
 bility and interpretability at the core of our deep learning methods, we m
 ake the endeavor reproducible.\n\nTag: Applications, Deep Learning, Exasca
 le\n\nRegistration Category: Workshop Reg Pass\n\nSession Chairs: Thomas J
 . Barr (Nationwide Children's Hospital); Patricia Kovatch (Icahn School of
  Medicine at Mount Sinai); and Eric Stahlberg (MD Anderson Cancer Center, 
 University of Texas)\n\n
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