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DTSTAMP:20220123T161734Z
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DTSTART;TZID=America/Vancouver:20220120T110000
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DESCRIPTION:This is an IEEE EMBS Vancouver Chapter and Biomedical Imaging &amp;
  AI joint event.\n\nPresenter: Professor Anne Martel\, Medical Biophysics\
 , University of Toronto\, ON\, Canada\n\nAbstract: The introduction of sca
 nners that are capable of digitizing microscopic slides at high magnificat
 ion has led to an explosion of interest in computational pathology in gene
 ral and deep learning applied to whole slide images (WSIs) in particular. 
 In my lab at Sunnybrook\, we are developing AI models that can detect canc
 er\, automatically segment regions of interest\, and learn predictive and 
 prognostic models that can be used to guide treatment decisions.\nIn this 
 talk I will outline some of the unique challenges of working with these ex
 tremely large WSIs and discuss some of the approaches that we have develop
 ed to overcome the problems of sparse annotations and weak\, noisy labels\
 , including self-supervision and multiple instance learning. I will also o
 utline some of the challenges in deploying AI algorithms to the clinic.\n\
 nCo-sponsored by: Biomedical Imaging and AI UBC\n\nSpeaker(s): Prof. Anne 
 Martel\, \n\nVancouver\, British Columbia\, Canada\, Virtual: https://even
 ts.vtools.ieee.org/m/290155
LOCATION:Vancouver\, British Columbia\, Canada\, Virtual: https://events.vt
 ools.ieee.org/m/290155
ORGANIZER:purang@ece.ubc.ca
SEQUENCE:7
SUMMARY:Invited Lecture: Artificial Intelligence and Digital pathology: Dea
 ling with the Annotation Bottleneck
URL;VALUE=URI:https://events.vtools.ieee.org/m/290155
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;This is an IEEE EMBS Vancouver Chapter and
  Biomedical Imaging &amp;amp\; AI joint event.&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;&lt;strong
 &gt;Presenter: &lt;/strong&gt;Professor Anne Martel\, Medical Biophysics\, Universi
 ty of Toronto\, ON\, Canada&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Abstract:&amp;nbsp
 \;&lt;/strong&gt;The introduction of scanners that are capable of digitizing mic
 roscopic slides at high magnification has led to an explosion of interest 
 in computational pathology in general and deep learning applied to whole s
 lide images (WSIs) in particular. In my lab at Sunnybrook\, we are develop
 ing AI models that can detect cancer\, automatically segment regions of in
 terest\, and learn predictive and prognostic models that can be used to gu
 ide treatment decisions.&lt;/p&gt;\n&lt;div&gt;In this talk I will outline some of the
  unique challenges of working with these extremely large WSIs and discuss 
 some of the approaches that we have developed to overcome the problems of 
 sparse annotations and weak\, noisy labels\, including self-supervision an
 d multiple instance learning. I will also outline some of the challenges i
 n deploying AI algorithms to the clinic.&lt;/div&gt;
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