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DTSTART;TZID=US/Pacific:20190729T183000
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DESCRIPTION:Despite the growing popularity of deep learning neural networks
  for various medical imaging applications\, the vast majority of algorithm
 s to date represent early proof-of-concept designs that will require a deg
 ree of evolution before achieving practical clinical utility. In this talk
 \, we explore several different ways in which the current generation of de
 ep learning applications can be advanced including:\n\n(1) Reformulating t
 he question: in medicine\, there are often times more than one way to ask 
 the same question---how do we reformulate a task in a way that both maximi
 zes clinical utility and also best leverages the strength of various deep 
 learning algorithms?\n\n(2) Customizing deep learning algorithms: what are
  the unique technical challenges posed by medical imaging data and how we 
 design custom deep learning architectures to account for them?\n\n(3) Clin
 ical implementation: what are some practical experiences learned from impl
 ementing deep learning tools in the clinical setting\, and what are some r
 egulatory hurdles that will need to be considered?\n\nSpeaker(s): Peter D.
  Chang\, MD\, \n\nAgenda: \n6:30pm - 7pm (Networking and Food)\n\n7pm - 8p
 m (Talk)\n\nRoom: Community Meeting Room\, 15500 Sand Canyon Ave\, Irvine\
 , California\, United States\, 92618
LOCATION:Room: Community Meeting Room\, 15500 Sand Canyon Ave\, Irvine\, Ca
 lifornia\, United States\, 92618
ORGANIZER:paulmathew.ieee@gmail.com
SEQUENCE:1
SUMMARY:IEEE–OC EMBS Chapter Presents “Advances in Deep Learning for Me
 dical Imaging”
URL;VALUE=URI:https://events.vtools.ieee.org/m/199951
X-ALT-DESC:Description: <br /><p style="font-weight: 400\;">Despite the gro
 wing popularity of deep learning neural networks for various medical imagi
 ng applications\, the vast majority of algorithms to date represent early 
 proof-of-concept designs that will require a degree of evolution before ac
 hieving practical clinical utility. In this talk\, we explore several diff
 erent ways in which the current generation of deep learning applications c
 an be advanced including:</p>\n<p style="font-weight: 400\;">(1)&nbsp\;Ref
 ormulating the question: in medicine\, there are often times more than one
  way to ask the same question---how do we reformulate a task in a way that
  both maximizes clinical utility and also best leverages the strength of v
 arious deep learning algorithms?</p>\n<p style="font-weight: 400\;">(2)&nb
 sp\;Customizing deep learning algorithms: what are the unique technical ch
 allenges posed by medical imaging data and how we design custom deep learn
 ing architectures to account for them?</p>\n<p style="font-weight: 400\;">
 (3)&nbsp\;Clinical implementation: what are some practical experiences lea
 rned from implementing deep learning tools in the clinical setting\, and w
 hat are some regulatory hurdles that will need to be considered?</p><br />
 <br />Agenda: <br /><p style="font-weight: 400\;">6:30pm - 7pm (Networking
  and Food)</p>\n<p style="font-weight: 400\;">7pm - 8pm (Talk)</p>
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