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DTSTART;TZID=US/Eastern:20260304T123000
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DESCRIPTION:AI and machine learning\, deep learning in particular\, have ma
 de significant progress recently\, revolutionizing the medical computing p
 ractice. In the past decades\, I have been collaborating closely with medi
 cal experts in the NYC region\, such as Mt. Sinai Hospital\, Memorial Sloa
 n-Kettering Institute\, Columbia Univ. medical school\, Yale Univ. Medical
  School\, and Johns Hopkins Univ.\, on a broad array of AI-enabled medical
  computing projects. In this talk\, I will introduce my recent work on the
  use of AI in cancer treatment and remote simultaneous medical interpretat
 ions. First\, a broad array of different medical modalities has to be effe
 ctively employed to effect better medical data analysis\, whereof enabling
  computer graphics\, signal processing\, and computer vision techniques mu
 st be called in. We developed a unified GUI-based system to put multimodal
  medical together for better cancer treatment planning purposes. In a seco
 nd project\, by using robust curve fitting and space/time local and global
  analysis\, we devised an effective human lung respiratory movement tracki
 ng algorithm with outstanding performance\, so that the radiation therapy 
 can be more effective. Next\, a mixture of experts\, each of which is a CN
 N-based UNet algorithm\, is introduced to effectively single out the cance
 r region. Our ongoing smart and cheap non-invasive bio-sign sensing device
 s and algorithms are next introduced to ensure more precise and non-invasi
 ve sensing can be achieved for more patients. Finally\, the Large Language
  Models (LLMs) are exploited to achieve remote simultaneous medical interp
 retation\, riding on the immense power of recent progress in LLMs. From th
 ese concrete projects\, it can be seen that mathematical/statistical model
 ing\, data structure and algorithm pipeline development\, creative use of 
 multimodal computing\, and various AI/ML algorithms can be incorporated to
  achieve improved healthcare. Effective use of AI/ML\, including current L
 LM and various foundation models\, has a bright future in medical computin
 g.\n\nCo-sponsored by: Fairleigh Dickinson University\n\nSpeaker(s): Jie W
 ei\, \n\nAgenda: \nIEEE North Jersey Section Computer Chapter and Signal P
 rocessing Chapter Seminar\n\nTitle: AI in Medical Computing: Cancer Treatm
 ent and Remote Simultaneous Medical Interpretations\n\nSpeaker: Prof. Jie 
 Wei\, Department of Computer Science\, CCNY\n\nTime: 12:30pm-1:30pm\n\nFai
 rleigh Dickinson University\n\n1000 River Road\, Building: Becton Hall\, R
 oom Number: 205\n\nTeaneck\, New Jersey\, United States 07666\n\nFor addit
 ional information about the venue and parking\, please contact\n\nDr. Hong
  Zhao\n\nzhao@fdu.edu\n\nBldg: Becton Hall 205\, 1000 River Road\, Teaneck
 \, New Jersey\, United States\, 07666
LOCATION:Bldg: Becton Hall 205\, 1000 River Road\, Teaneck\, New Jersey\, U
 nited States\, 07666
ORGANIZER:zhao@fdu.edu
SEQUENCE:33
SUMMARY:AI in Medical Computing: Cancer Treatment and Remote Simultaneous M
 edical Interpretations
URL;VALUE=URI:https://events.vtools.ieee.org/m/535693
X-ALT-DESC:Description: &lt;br /&gt;&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-bottom: 8.
 0pt\; text-align: justify\;&quot;&gt;&lt;span style=&quot;font-size: 14pt\; line-height: 1
 15%\; font-family: &#39;times new roman&#39;\, times\, serif\; color: rgb(51\, 51\
 , 51)\; letter-spacing: 0.25pt\; background: white\;&quot;&gt;AI and machine learn
 ing\, deep learning in particular\, have made significant progress recentl
 y\, revolutionizing the medical computing practice. In the past decades\, 
 I have been collaborating closely with medical experts in the NYC region\,
  such as Mt. Sinai Hospital\, Memorial Sloan-Kettering Institute\, Columbi
 a Univ. medical school\, Yale Univ. Medical School\, and Johns Hopkins Uni
 v.\, on a broad array of AI-enabled medical computing projects. In this ta
 lk\, I will introduce my recent work on the use of AI in cancer treatment 
 and remote simultaneous medical interpretations. First\, a broad array of 
 different medical modalities has to be effectively employed to effect bett
 er medical data analysis\, whereof enabling computer graphics\, signal pro
 cessing\, and computer vision techniques must be called in. We developed a
  unified GUI-based system to put multimodal medical together for better ca
 ncer treatment planning purposes. In a second project\, by using robust cu
 rve fitting and space/time local and global analysis\, we devised an effec
 tive human lung respiratory movement tracking algorithm with outstanding p
 erformance\, so that the radiation therapy can be more effective. Next\, a
  mixture of experts\, each of which is a CNN-based UNet algorithm\, is int
 roduced to effectively single out the cancer region. Our ongoing smart and
  cheap non-invasive bio-sign sensing devices and algorithms are next intro
 duced to ensure more precise and non-invasive sensing can be achieved for 
 more patients. Finally\, the Large Language Models (LLMs) are exploited to
  achieve remote simultaneous medical interpretation\, riding on the immens
 e power of recent progress in LLMs. From these concrete projects\, it can 
 be seen that mathematical/statistical modeling\, data structure and algori
 thm pipeline development\, creative use of multimodal computing\, and vari
 ous AI/ML algorithms can be incorporated to achieve improved healthcare. E
 ffective use of AI/ML\, including current LLM and various foundation model
 s\, has a bright future in medical computing.&amp;nbsp\;&lt;/span&gt;&lt;/p&gt;&lt;br /&gt;&lt;br /
 &gt;Agenda: &lt;br /&gt;&lt;p&gt;IEEE North Jersey Section Computer Chapter and Signal Pr
 ocessing Chapter Seminar&lt;/p&gt;\n&lt;p&gt;Title: &lt;span style=&quot;font-size: 14pt\;&quot;&gt;&lt;s
 trong&gt;&lt;span style=&quot;line-height: 115%\; font-family: &#39;Times New Roman&#39;\, se
 rif\; color: rgb(51\, 51\, 51)\; letter-spacing: 0.25pt\; background: whit
 e\;&quot;&gt;AI in Medical Computing: Cancer Treatment and Remote Simultaneous Med
 ical Interpretations&lt;/span&gt;&lt;/strong&gt;&lt;/span&gt;&lt;/p&gt;\n&lt;p&gt;Speaker: Prof. Jie Wei
 \,&amp;nbsp\; Department of Computer Science\, CCNY&lt;/p&gt;\n&lt;p&gt;Time: 12:30pm-1:30
 pm&lt;/p&gt;\n&lt;p&gt;Fairleigh Dickinson University&lt;/p&gt;\n&lt;p&gt;1000 River Road\, &amp;nbsp\
 ;&lt;span class=&quot;sublabel&quot;&gt;Building:&lt;/span&gt; Becton Hall\,&amp;nbsp\;&lt;span class=&quot;
 sublabel&quot;&gt;Room Number:&lt;/span&gt; 205&lt;/p&gt;\n&lt;p&gt;Teaneck\, New Jersey\, United St
 ates 07666&lt;/p&gt;\n&lt;p&gt;For additional information about the venue and parking\
 , please contact&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Dr. Hong Zhao&lt;/strong&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;&lt;a 
 href=&quot;mailto:zhao@fdu.edu&quot; target=&quot;_blank&quot; rel=&quot;nofollow noopener noreferr
 er&quot;&gt;zhao@fdu.edu&lt;/a&gt;&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n
 &lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;
 &lt;/p&gt;
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