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VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:Asia/Kolkata
BEGIN:STANDARD
DTSTART:19451014T230000
TZOFFSETFROM:+0630
TZOFFSETTO:+0530
TZNAME:IST
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BEGIN:VEVENT
DTSTAMP:20250508T153936Z
UID:571B0105-575C-4ECF-A3D3-2C916C8F3A66
DTSTART;TZID=Asia/Kolkata:20250218T150000
DTEND;TZID=Asia/Kolkata:20250218T160000
DESCRIPTION:Deep learning has attracted great attention in the medical imag
 ing community as a promising solution for automated\, fast and accurate me
 dical image analysis\, which is mandatory for quality healthcare. Convolut
 ional neural networks and its variants have become the most preferred and 
 widely used deep learning models in medical image analysis. U-Net is anoth
 er popular architecture especially for biomedical imaging. It consists of 
 a contraction and expansion path to pixel-wise predict the dataset. This m
 odel is better than previously available medical image segmentation approa
 ches. However again\, it fails to produce the promising results with 3D vo
 xels. For that\, an incremental version of U-Net\, Multiplanar U-Net has b
 een developed in 2019. In this talk\, the speaker will discuss about multi
 -planar 3D knee MRI segmentation architectures\, which is one of the U-Net
  inspired models.\n\nSpeaker(s): Dr. Sandeep Singh Sengar\n\nVirtual: http
 s://events.vtools.ieee.org/m/469286
LOCATION:Virtual: https://events.vtools.ieee.org/m/469286
ORGANIZER:ieee.embs.sbc.iitkgp@gmail.com
SEQUENCE:11
SUMMARY:Deep Learning for Medical Image Analysis
URL;VALUE=URI:https://events.vtools.ieee.org/m/469286
X-ALT-DESC:Description: <br /><p><span lang="EN" style="font-size: 11.0pt\;
  line-height: 115%\; font-family: 'Arial'\,sans-serif\; mso-fareast-font-f
 amily: Arial\; mso-ansi-language: EN\; mso-fareast-language: EN-GB\; mso-b
 idi-language: AR-SA\;">Deep learning has attracted great attention in the 
 medical imaging community as a promising solution for automated\, fast and
  accurate medical image analysis\, which is mandatory for quality healthca
 re. Convolutional neural networks and its variants have become the most pr
 eferred and widely used deep learning models in medical image analysis. U-
 Net is another popular architecture especially for biomedical imaging. It 
 consists of a contraction and expansion path to pixel-wise predict the dat
 aset. This model is better than previously available medical image segment
 ation approaches. However again\, it fails to produce the promising result
 s with 3D voxels. For that\, an incremental version of U-Net\, Multiplanar
  U-Net has been developed in 2019. In this talk\, the speaker will discuss
  about multi-planar 3D knee MRI segmentation architectures\, which is one 
 of the U-Net inspired models. </span></p>
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