Medical Image Segmentation using Deep Learning approaches (Transformers in Medical Segmentation

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Medical Image Segmentation is a challenging area of research where Deep Learning algorithms have shown lot of success in recent times.

In this talk, we would get an introduction to Deep Learning based segmentation of Medical images. A brief overview of UNets would be covered to introduce the audience to segmentation architectures.The second part of the talk would be on Attention mechanism.  Attention mechanism has revolutionized Deep Learning algorithms, especially in NLP domain and more recently in Computer Vision tasks.In this talk, after a brief introduction to attention blocks (from NLP context) we start with some of the attention approachesor computer vision tasks. We dive in more detail on couple of attention blocks – criss-cross attention and attention-augmented-convolution.Some of the active frontiers in attention blocks (deformable attention) would be finally discussed to give some insights on current research directions.

 



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  • Date: 19 Oct 2022
  • Time: 07:00 PM to 07:59 PM
  • All times are (UTC+05:30) Chennai
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registration is in progress

  • Starts 14 October 2022 11:08 AM
  • Ends 18 October 2022 11:08 AM
  • All times are (UTC+05:30) Chennai
  • No Admission Charge


  Speakers

Dr.Kumar Rajamani Dr.Kumar Rajamani

Topic:

Medical Image Segmentation using Deep Learning approaches (Transformers in Medical Segmentation)

In this talk, we would get an introduction to Deep Learning based segmentation of Medical images. A brief overview of UNets would be covered to introduce the audience to segmentation architectures.The second part of the talk would be on Attention mechanism.  Attention mechanism has revolutionized Deep Learning algorithms, especially in NLP domain and more recently in Computer Vision tasks.In this talk, after a brief introduction to attention blocks (from NLP context) we start with some of the attention approachesor computer vision tasks. We dive in more detail on couple of attention blocks – criss-cross attention and attention-augmented-convolution.Some of the active frontiers in attention blocks (deformable attention) would be finally discussed to give some insights on current research directions.

Biography:

Dr. Kumar Rajamani is Senior Manager Algorithm at KLA Tencor. Prior to this, he was Senior Scientist at Philips Research. 

He completed his Postdoctoral Researcher at Institute of Medical Informatics, University of Luebeck,Germany.


His research during the Postdoctoral stint was on Medical Deep learning, specifically advanced architectures for medical image segmentation (COVID-19 lesion segmentation) using attention mechanisms. 

 

Kumar was earlier with Robert Bosch, GE Global Research (GRC), Philips Research and Amrita University. His research focus includes Semi-conductor Wafer Inspection, Metrology, Image Analysis,MedicalImaging. 


He has nine patents to his credit. Kumar completed his Ph.D. in Biomedical Engineering from University of Bern, Switzerland.

 

Address:Hyderabad, India