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TZID:America/Chicago
X-LIC-LOCATION:America/Chicago
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TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
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DTSTART:19701101T020000
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DTSTAMP:20260522T150115Z
LOCATION:D165
DTSTART;TZID=America/Chicago:20181111T153000
DTEND;TZID=America/Chicago:20181111T160000
UID:submissions.supercomputing.org_SC18_sess147_ws_cafcw107@linklings.com
SUMMARY:HPC-Based Hyperparameter Search of MT-CNN for Information Extracti
 on from Cancer Pathology Reports
DESCRIPTION:Hong-Jun Yoon, Mohammed Alawad, Blair Christian, Jacob Hinkle,
  Arvind Ramanathan, and Georgia Tourassi (Oak Ridge National Laboratory)\n
 \nFinding optimal hyperparameters is necessary to identify the best perfor
 ming deep learning models, but the process is costly. In this paper, we ap
 plied model-based optimization, also known as Bayesian optimization, using
  the CANDLE framework implemented on a High-Performance Computing environm
 ent. As a use case, we selected information extraction from cancer patholo
 gy reports using a multi-task convolutional neural network, a model with 1
 0 hyperparameters to be optimized. We utilized a synthesized text corpus o
 f 8,000 training cases and 2,000 validation cases with four types of clini
 cal task labels including primary cancer site, laterality, behavior, and h
 istological grade. We conducted the experiments on the Titan supercomputer
  at the Oak Ridge Leadership Computing Facility (OLCF), reported the optim
 al hyperparameters found, and demonstrated that hyperparameter optimizatio
 n using the CANDLE framework is a feasible approach with respect to both s
 calability and clinical task performance.\n\nTag: Applications, Deep Learn
 ing, Exascale\n\nRegistration Category: Workshop Reg Pass\n\nSession Chair
 s: Thomas J. Barr (Nationwide Children's Hospital); Patricia Kovatch (Icah
 n School of Medicine at Mount Sinai); and Eric Stahlberg (MD Anderson Canc
 er Center, University of Texas)\n\n
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