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DTSTART:20200308T030000
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DTSTAMP:20201005T174547Z
UID:005D2E85-2849-407A-A77E-254B9704A268
DTSTART;TZID=Canada/Eastern:20201001T143000
DTEND;TZID=Canada/Eastern:20201001T153000
DESCRIPTION:Medical imaging\, (e.g.\, computed tomography (CT)\, magnetic r
 esonance imaging (MRI)\, positron emission tomography (PET)\, mammography\
 , ultrasound\, X-ray) has advanced at a rapid speed over last decades. Cur
 rently\, the medical image interpretation is mostly performed by human exp
 erts\, which is a tedious task and subject to high inter-operator variabil
 ity. Deep learning is providing exciting solutions for automated medical i
 mage analysis problems. Recent advances in deep learning have helped to id
 entify\, classify\, and quantify patterns in medical images. In this semin
 ar\, the principles and methods of deep learning concepts\, particularly c
 onvolutional neural network (CNN) will be introduced. I will describe seve
 ral interesting applications of deep learning for medical image analysis\,
  including my recent works on segmenting myocardial scar (injured) tissue 
 in the heart\, prostate tumor detection\, and kidney lesion localization i
 n 3D MRI and CT images.\n\nSpeaker(s): Dr. Fatemeh Zabihollahy\, \n\nOttaw
 a\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/240940
LOCATION:Ottawa\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.or
 g/m/240940
ORGANIZER:m.abdelazez.ca@ieee.org
SEQUENCE:1
SUMMARY:Deep Learning Methods for Computed Tomography and Magnetic Resonanc
 e Images
URL;VALUE=URI:https://events.vtools.ieee.org/m/240940
X-ALT-DESC:Description: <br /><p>Medical imaging\, (e.g.\, computed tomogra
 phy (CT)\, magnetic resonance imaging (MRI)\, positron emission tomography
  (PET)\, mammography\, ultrasound\, X-ray) has advanced at a rapid speed o
 ver last decades. Currently\, the medical image interpretation is mostly p
 erformed by human experts\, which is a tedious task and subject to high in
 ter-operator variability. Deep learning is providing exciting solutions fo
 r automated medical image analysis problems. Recent advances in deep learn
 ing have helped to identify\, classify\, and quantify patterns in medical 
 images. In this seminar\, the principles and methods of deep learning conc
 epts\, particularly convolutional neural network (CNN) will be introduced.
  I will describe several interesting applications of deep learning for med
 ical image analysis\, including my recent works on segmenting myocardial s
 car (injured) tissue in the heart\, prostate tumor detection\, and kidney 
 lesion localization in 3D MRI and CT images.</p>
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