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NVIDIA- Deep Learning for Healthcare Image Analysis (DL-HIA)

NVIDIA
1 Days
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$500.00
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Course Summary
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Description

You’ll learn how to apply deep learning to radiology and medical imaging in this class. The workshop covers: 
 

  •  image segmentation, which classifies each pixel into a specific class
  •  Training a Convolutional neural networks (CNNs) to infer the volume of the left ventricle of the human heart from time-series MRI data
  •  Understand techniques to use radiomics(imaging genomics) to identify the genomics of a disease.

Ideally, students in this course has a basic familiarity with deep neural networks, either through the DLI Fundamentals of Computer Vision course or another online training program. Basic coding experience in python or a similar language is also useful. 

Objectives

Learn to apply CNNs to MRI scans plus: 
 

  • Perform image segmentation on MRI images to determine the location of the left ventricle
  • Calculate ejection fractions by measuring differences between diastole and systole using CNNs applied to MRI scans to detect heart disease
  • Apply CNNs to MRI scans of low-grade gliomas (LGGs) to determine 1p/19q chromosome co-deletion status 

Outline

Introduction (15 min) 

  •  Welcome Introductions, account creation, and troubleshooting 

Image Segmentation (120 min) 

  • Extend Caffe with custom Python layers
  • Implement the process of transfer learning
  • Create fully convolutional neural networks from popular image classification networks 

 Learn techniques for placing each pixel of an image into a specific class  

 
 ​Image Analysis (120 min) 

  • Extend a canonical 2D CNN to more complex data
  • Use the framework MXNet through the standard Python API and through R
  • Process high-dimensionality imagery that may be volumetric and have a temporal component 


Leverage Convolutional neural networks (CNNs) for medical image analysis to infer patient status from non-visible images. Train a CNN to infer the volume of the left ventricle of the human heart from time-series MRI data  
  
Image Classification with TensorFlow (120 min) 

  • ● Design and train Convolutional Neural Networks (CNNs)
  • ● Use imaging genomics (radiomics) to create biomarkers that identify the genomics of a disease without the use of an invasive biopsy
  • ● Explore the radiogenomics work being done at the Mayo Clinic


Learn about the work  being performed at the Mayo Clinic, using deep learning techniques to detect Radiomics from MRI imaging that has led to more effective treatments and better health outcomes for patients with brain tumors 


Closing remarks (15 min) 

  • Wrap-up with the potential next steps and Q&A 

A quick overview of the next -steps you could leverage to build and deploy your own applications and any Q&A