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DTSTAMP:20230808T220858Z
UID:FB9C4107-06C5-47DB-A961-379E9BDF45DC
DTSTART;TZID=America/New_York:20230710T093000
DTEND;TZID=America/New_York:20230714T130000
DESCRIPTION:Computer vision as a subfield of AI has been around for several
  years dealing with how computers can understand from digital images and v
 ideo sequences. Advanced computer vision algorithms have already demonstra
 ted successful applications in a variety of domains\, including medical im
 age interpretation\, remote surgery\, surveillance systems\, security and 
 biometrics\, autonomous vehicles\, and scene reconstruction\, purposing to
  name a few. There is a list of fascinating problems in applied computer v
 ision in medical imaging\, with object detection and localization being on
 e of the most interesting ones. Object detection and localization is now a
 lso widely associated with self-driving cars where automatic systems combi
 ne computer vision\, LIDAR\, and GPUs to generate a multidimensional repre
 sentation of the road with all its participants. It is also commonly used 
 in medical image analysis\, video surveillance and monitoring\, counting p
 eople for general statistics\, and computationally analyze customer experi
 ence with walking patterns within shopping centers.\n\nIn this summer scho
 ol\, you will learn -from scratch- how to use advanced computer vision alg
 orithms to tackle the problem of object detection and localization in medi
 cal images. We will discuss object detection mechanism(s) in practice with
  several hands-on-practices starting from manual image annotation to progr
 amming and implementation in Python. We\, together\, will explore what obj
 ect detection computational vision algorithm is\, what is does\, and how. 
 The current mini summer camp at the University of Pittsburgh is structured
  such that in addition to attending lectures\, the students will be also w
 orking in teams on a project assignment.\n\nTopics included but not limite
 d to:\n\n- Introduction to Computer Vision\n- Introduction to Deep Learnin
 g Computer Vision\n- Deep Convolutional Neural Networks (CNNs)\n- Introduc
 tion to Object Detection and Localization in Computer Vision\n- Introducti
 on to PyTorch\n- Manual Annotation of Medical Images using the LabelImg To
 olset\n- Sliding Windows and Bounding Boxes in Object Detection\n- Non-max
  Suppression\n- YOLO (You Only Look Once) and SSD (Single Shot Detector)\n
 \n25 seats in-person and 25 seats virtual (Zoom) are available on a first-
 come-first-serve basis.\n\nCo-sponsored by: Dr Ahmad Tafti\, Pitt HexAI Re
 search Laboratory at the University of Pittsburgh School of Health and Reh
 abilitation Sciences\n\nSpeaker(s): Ahmad\, Soheyla\n\nAgenda: \nSchedule:
 \n\nDay\n\nTime\n\nAgendas\n\nMonday\, July 10th\n\n9:30 – 10:30\n\nIntr
 oduction to Computer Vision\n\n10:30 – 10:45\n\nBreak\n\n10:45 – 11:45
 \n\nIntroduction to Deep Learning\n\n11:45 – 12:00\n\nBreak\n\n12:00 –
  13:00\n\nHands-on-Practice: Google Colab\; What and Why?\n\nTuesday\, Jul
 y 11th\n\n9:30 – 10:30\n\nDeep Convolutional Neural Networks (CNNs)\n\n1
 0:30 – 10:45\n\nBreak\n\n10:45 – 11:45\n\nIntroduction to PyTorch\n\n1
 1:45 – 12:00\n\nBreak\n\n12:00 – 13:00\n\nHands-on-Practice: Medical i
 mage annotation (manual annotation) using LabelImg\n\nWednesday\, July 12t
 h\n\n9:30 – 10:30\n\nSliding Windows and Convolutional Implementation of
  Sliding Windows\n\n10:30 – 10:45\n\nBreak\n\n10:45 – 11:45\n\nBoundin
 g Box Prediction and Intersection Over Union (IoU)\n\n11:45 – 12:00\n\nB
 reak\n\n12:00 – 13:00\n\nHands-on-Practice: OAI Imaging Dataset (https:/
 /nda.nih.gov/oai) plus Pizza and soft drinks!!!\n\nThursday\, July 13th\n\
 n9:30 – 10:30\n\nNon-Max Suppression\, YOLO (You Only Look Once) and SSD
  (Single Shot Detector)\n\n10:30 – 10:45\n\nBreak\n\n10:45 – 11:45\n\n
 Hands-on-Practice: Detection and localization of Total Knee Arthroplasty (
 TKA) implants in plain X-ray images\n\n11:45 – 12:00\n\nBreak\n\n12:00 
 – 12:30\n\nHands-on-Practice: Model analysis\; IoU\n\nFriday\, July 14th
 \n\n9:30 – 10:30\n\n· Project Definition and Team Building\n\n· Teams 
 will start working on their projects\n\n10:30 – 10:45\n\nBreak\n\n10:45 
 – 11:45\n\nTeams will be working and finalizing their projects\n\n11:45 
 – 12:00\n\nBreak\n\n12:00 – 13:00\n\nProject Presentation and Pizza!!!
 \n\nRoom: 6048\, Bldg: Forbes Tower\, 219 Meyran Avenue\, Pittsburgh\, Pen
 nsylvania\, United States\, 15213\, Virtual: https://events.vtools.ieee.or
 g/m/362022
LOCATION:Room: 6048\, Bldg: Forbes Tower\, 219 Meyran Avenue\, Pittsburgh\,
  Pennsylvania\, United States\, 15213\, Virtual: https://events.vtools.iee
 e.org/m/362022
ORGANIZER:tafti.ahmad@pitt.edu
SEQUENCE:17
SUMMARY:2023 Mini Summer Camp on Object Detection and Localization in Medic
 al Images using Artificial Intelligence (AI).
URL;VALUE=URI:https://events.vtools.ieee.org/m/362022
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;Computer vision as a subfield of AI has be
 en around for several years dealing with how computers can understand from
  digital images and video sequences. Advanced computer vision algorithms h
 ave already demonstrated successful applications in a variety of domains\,
  including medical image interpretation\, remote surgery\, surveillance sy
 stems\, security and biometrics\, autonomous vehicles\, and scene reconstr
 uction\, purposing to name a few. There is a list of fascinating problems 
 in applied computer vision in medical imaging\, with object detection and 
 localization being one of the most interesting ones. Object detection and 
 localization is now also widely associated with self-driving cars where au
 tomatic systems combine computer vision\, LIDAR\, and GPUs to generate a m
 ultidimensional representation of the road with all its participants. It i
 s also commonly used in medical image analysis\, video surveillance and mo
 nitoring\, counting people for general statistics\, and computationally an
 alyze customer experience with walking patterns within shopping centers.&lt;/
 p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;In this summer school\, you will learn -from scratc
 h- how to use advanced computer vision algorithms to tackle the problem of
  object detection and localization in medical images. We will discuss obje
 ct detection mechanism(s) in practice with several hands-on-practices star
 ting from manual image annotation to programming and implementation in Pyt
 hon. We\, together\, will explore what object detection computational visi
 on algorithm is\, what is does\, and how. The current mini summer camp at 
 the University of Pittsburgh is structured such that in addition to attend
 ing lectures\, the students will be also working in teams on a project ass
 ignment.&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Topics included but not limited t
 o:&lt;/strong&gt;&lt;/p&gt;\n&lt;ul&gt;\n&lt;li&gt;Introduction to Computer Vision&lt;/li&gt;\n&lt;li&gt;Intro
 duction to Deep Learning Computer Vision&lt;/li&gt;\n&lt;li&gt;Deep Convolutional Neur
 al Networks (CNNs)&lt;/li&gt;\n&lt;li&gt;Introduction to Object Detection and Localiza
 tion in Computer Vision&lt;/li&gt;\n&lt;li&gt;Introduction to PyTorch&lt;/li&gt;\n&lt;li&gt;Manual
  Annotation of Medical Images using the LabelImg Toolset&lt;/li&gt;\n&lt;li&gt;Sliding
  Windows and Bounding Boxes in Object Detection&lt;/li&gt;\n&lt;li&gt;Non-max Suppress
 ion&lt;/li&gt;\n&lt;li&gt;YOLO (You Only Look Once) and SSD (Single Shot Detector)&lt;/li
 &gt;\n&lt;/ul&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;25 seats in-person and 25 seats virtual (Zoom
 ) are available on a first-come-first-serve basis.&lt;/p&gt;&lt;br /&gt;&lt;br /&gt;Agenda: 
 &lt;br /&gt;&lt;p&gt;&lt;strong&gt;Schedule:&lt;/strong&gt;&lt;/p&gt;\n&lt;table&gt;\n&lt;tbody&gt;\n&lt;tr&gt;\n&lt;td width
 =&quot;134&quot;&gt;\n&lt;p&gt;&lt;strong&gt;Day&lt;/strong&gt;&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;93&quot;&gt;\n&lt;p&gt;&lt;strong&gt;T
 ime&lt;/strong&gt;&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;338&quot;&gt;\n&lt;p&gt;&lt;strong&gt;Agendas&lt;/strong&gt;&lt;/p&gt;
 \n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td rowspan=&quot;5&quot; width=&quot;134&quot;&gt;\n&lt;p&gt;Monday\, July 10th&lt;
 /p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;93&quot;&gt;\n&lt;p&gt;9:30 &amp;ndash\; 10:30&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=
 &quot;338&quot;&gt;\n&lt;p&gt;Introduction to Computer Vision&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td wi
 dth=&quot;93&quot;&gt;\n&lt;p&gt;10:30 &amp;ndash\; 10:45&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;338&quot;&gt;\n&lt;p&gt;Break&lt;
 /p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width=&quot;93&quot;&gt;\n&lt;p&gt;10:45 &amp;ndash\; 11:45&lt;/p&gt;\n&lt;/t
 d&gt;\n&lt;td width=&quot;338&quot;&gt;\n&lt;p&gt;Introduction to Deep Learning&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n
 &lt;tr&gt;\n&lt;td width=&quot;93&quot;&gt;\n&lt;p&gt;11:45 &amp;ndash\; 12:00&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;338&quot;
 &gt;\n&lt;p&gt;Break&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width=&quot;93&quot;&gt;\n&lt;p&gt;12:00 &amp;ndash\; 13
 :00&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;338&quot;&gt;\n&lt;p&gt;&lt;strong&gt;Hands-on-Practice:&lt;/strong&gt; G
 oogle Colab\; What and Why?&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td colspan=&quot;3&quot; width
 =&quot;566&quot;&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td rowspan=&quot;5&quot; width=&quot;134&quot;&gt;\
 n&lt;p&gt;Tuesday\, July 11th&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;93&quot;&gt;\n&lt;p&gt;9:30 &amp;ndash\; 10:3
 0&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;338&quot;&gt;\n&lt;p&gt;Deep Convolutional Neural Networks (CNN
 s)&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width=&quot;93&quot;&gt;\n&lt;p&gt;10:30 &amp;ndash\; 10:45&lt;/p&gt;\n
 &lt;/td&gt;\n&lt;td width=&quot;338&quot;&gt;\n&lt;p&gt;Break&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width=&quot;93&quot;&gt;
 \n&lt;p&gt;10:45 &amp;ndash\; 11:45&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;338&quot;&gt;\n&lt;p&gt;Introduction to
  PyTorch&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width=&quot;93&quot;&gt;\n&lt;p&gt;11:45 &amp;ndash\; 12:00
 &lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;338&quot;&gt;\n&lt;p&gt;Break&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width
 =&quot;93&quot;&gt;\n&lt;p&gt;12:00 &amp;ndash\; 13:00&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;338&quot;&gt;\n&lt;p&gt;&lt;strong&gt;H
 ands-on-Practice:&lt;/strong&gt; Medical image annotation (manual annotation) us
 ing LabelImg&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td colspan=&quot;3&quot; width=&quot;566&quot;&gt;\n&lt;p&gt;&amp;nb
 sp\;&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td rowspan=&quot;5&quot; width=&quot;134&quot;&gt;\n&lt;p&gt;Wednesday\,
  July 12&lt;sup&gt;th&lt;/sup&gt;&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;93&quot;&gt;\n&lt;p&gt;9:30 &amp;ndash\; 10:30&lt;
 /p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;338&quot;&gt;\n&lt;p&gt;Sliding Windows and Convolutional Impleme
 ntation of Sliding Windows&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width=&quot;93&quot;&gt;\n&lt;p&gt;10
 :30 &amp;ndash\; 10:45&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;338&quot;&gt;\n&lt;p&gt;Break&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr
 &gt;\n&lt;tr&gt;\n&lt;td width=&quot;93&quot;&gt;\n&lt;p&gt;10:45 &amp;ndash\; 11:45&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;3
 38&quot;&gt;\n&lt;p&gt;Bounding Box Prediction and Intersection Over Union (IoU)&lt;/p&gt;\n&lt;/
 td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width=&quot;93&quot;&gt;\n&lt;p&gt;11:45 &amp;ndash\; 12:00&lt;/p&gt;\n&lt;/td&gt;\n&lt;td
  width=&quot;338&quot;&gt;\n&lt;p&gt;Break&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width=&quot;93&quot;&gt;\n&lt;p&gt;12:00
  &amp;ndash\; 13:00&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;338&quot;&gt;\n&lt;p&gt;&lt;strong&gt;Hands-on-Practice
 :&lt;/strong&gt; OAI Imaging Dataset (&lt;a href=&quot;https://nda.nih.gov/oai&quot;&gt;https://
 nda.nih.gov/oai&lt;/a&gt;) plus Pizza and soft drinks!!!&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;
 \n&lt;td colspan=&quot;3&quot; width=&quot;566&quot;&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td ro
 wspan=&quot;5&quot; width=&quot;134&quot;&gt;\n&lt;p&gt;Thursday\, July 13&lt;sup&gt;th&lt;/sup&gt;&lt;/p&gt;\n&lt;/td&gt;\n&lt;td
  width=&quot;93&quot;&gt;\n&lt;p&gt;9:30 &amp;ndash\; 10:30&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;338&quot;&gt;\n&lt;p&gt;Non-
 Max Suppression\, YOLO (You Only Look Once) and SSD (Single Shot Detector)
 &lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width=&quot;93&quot;&gt;\n&lt;p&gt;10:30 &amp;ndash\; 10:45&lt;/p&gt;\n&lt;/
 td&gt;\n&lt;td width=&quot;338&quot;&gt;\n&lt;p&gt;Break&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width=&quot;93&quot;&gt;\n
 &lt;p&gt;10:45 &amp;ndash\; 11:45&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;338&quot;&gt;\n&lt;p&gt;&lt;strong&gt;Hands-on-
 Practice:&lt;/strong&gt; Detection and localization of Total Knee Arthroplasty (
 TKA) implants in plain X-ray images&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width=&quot;93
 &quot;&gt;\n&lt;p&gt;11:45 &amp;ndash\; 12:00&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;338&quot;&gt;\n&lt;p&gt;Break&lt;/p&gt;\n&lt;/
 td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width=&quot;93&quot;&gt;\n&lt;p&gt;12:00 &amp;ndash\; 12:30&lt;/p&gt;\n&lt;/td&gt;\n&lt;td
  width=&quot;338&quot;&gt;\n&lt;p&gt;&lt;strong&gt;Hands-on-Practice:&lt;/strong&gt; Model analysis\; IoU
 &lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td colspan=&quot;3&quot; width=&quot;566&quot;&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;/
 td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td rowspan=&quot;5&quot; width=&quot;134&quot;&gt;\n&lt;p&gt;Friday\, July 14th&lt;/p&gt;\
 n&lt;/td&gt;\n&lt;td width=&quot;93&quot;&gt;\n&lt;p&gt;9:30 &amp;ndash\; 10:30&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;338
 &quot;&gt;\n&lt;p&gt;&amp;middot\;&amp;nbsp\;&amp;nbsp\;&amp;nbsp\;&amp;nbsp\;&amp;nbsp\;&amp;nbsp\; Project Definit
 ion and Team Building&lt;/p&gt;\n&lt;p&gt;&amp;middot\;&amp;nbsp\;&amp;nbsp\;&amp;nbsp\;&amp;nbsp\;&amp;nbsp\;
 &amp;nbsp\; Teams will start working on their projects&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;
 \n&lt;td width=&quot;93&quot;&gt;\n&lt;p&gt;10:30 &amp;ndash\; 10:45&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;338&quot;&gt;\n&lt;
 p&gt;Break&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width=&quot;93&quot;&gt;\n&lt;p&gt;10:45 &amp;ndash\; 11:45&lt;
 /p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;338&quot;&gt;\n&lt;p&gt;Teams will be working and finalizing thei
 r projects&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width=&quot;93&quot;&gt;\n&lt;p&gt;11:45 &amp;ndash\; 12:
 00&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;338&quot;&gt;\n&lt;p&gt;Break&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td wid
 th=&quot;93&quot;&gt;\n&lt;p&gt;12:00 &amp;ndash\; 13:00&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;338&quot;&gt;\n&lt;p&gt;Project
  Presentation and Pizza!!!&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;/tbody&gt;\n&lt;/table&gt;\n&lt;p&gt;&amp;nbsp
 \;&lt;/p&gt;
END:VEVENT
END:VCALENDAR