Deep Learning for Healthcare Image Analysis Training

Basic familiarity with deep neural networks, Basic coding experience in python or a similar language

Learning 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

Why Deep Learning Institute Hands-on Training?

• Learn to build deep learning and accelerated computing applications across a wide range of industry segments such as Autonomous Vehicles, Digital Content Creation, Finance, Game Development, and Healthcare
• Obtain guided hands-on experience using the most widely used, industry-standard software, tools, and frameworks
• Acquire real-world expertise through content designed in collaboration with industry leaders such as the Children’s Hospital of Los Angeles, Mayo Clinic, and PwC
• Earn NVIDIA DLI Certification to prove your subject matter competency and support professional career growth
• Access content anywhere, anytime with a fully configured GPU-accelerated workstation in the
• cloud

Certification

• Students receive NVIDIA DLI Certification upon completion to prove subject matter competency and support professional career growth

Introduction

• Welcome

Introductions, account creation, and troubleshooting

Image Segmentation

• 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

• 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

• 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

• 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