Deep Learning for Healthcare Image Analysis

3 Labs · 60 Credits · 4h 45m

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Learn how to apply Convolutional Neural Networks (CNNs) to MRI scans to perform a variety of medical tasks and calculations. Upon completion of this course, you’ll be able to apply CNNs to MRI scans to conduct a variety of medical tasks.

Medical Image Segmentation Using DIGITS

This label explores various approaches to the problem of semantic image segmentation, which is a generalization of image classification where class predictions are made at the pixel level. In this lab we will use the Sunnybrook Cardiac Data to train a neural network to learn to locate the left ventricle on MRI images. On completion of this lab, you will understand how to use popular image classification neural networks for semantic segmentation, you will learn how to extend Caffe with custom Python layers, you will become familiar with the concept of transfer learning and you will get to train two neural networks from the family of Fully Convolutional Networks (FCN).

Icon  intro introductory 1 Token 1 Hour 30 Minutes

Medical Image Analysis with R and MXNet

The primary purpose of this lab is to explore the second annual national data science bowl (NDSB2). The challenge posed by NDSB2 was to estimate ejection fraction from a sequence of MRI derived images of a beating heart. In essence, the ejection fraction is the difference of the blood volume into the heart minus the blood volume out of the heart. That is, the volume of blood ejected from a human heart over a single beat (i.e. expansion and contraction). The general notion here is that an abnormal ejection fraction (too small or too large) is indicative of a serious medical condition. In a typical laboratory setting, it can take upwards of 20 mins for medical professionals to analyze a single pulmonary MRI scan -- no doubt time better spent instead with patients. For this qwiklab we use the popular R programming language with deep learning framework MXNet to create a powerful GPU accelerated convolution neural network (CNN) solution. This lab will outline the process of preparing a large image dataset for training as well as general considerations and common strategies for deep learning. With only this brief encounter, we will not be able to obtain the near human performance levels achieved by the NDSB2 competition finalists, however, tutorial alumni will take away the essential knowledge and basic skills to be successful in creating their own deep learning workflows. Finally, we hope this interaction raises awareness for applications of deep learning in healthcare and inspires participants to contribute their own ideas in the next national data science bowl.

Icon  level Intermediate 30 Credits 1 Hour 30 Minutes

Image Classification with TensorFlow: Radiomics - 1p19q Chromosome Status Classification

Thanks to work being performed at Mayo Clinic, approaches using deep learning techniques to detect Radiomics from MRI imaging can lead to more effective treatments and yield better health outcomes for patients with brain tumors. Radiogenomics, specifically Imaging Genomics, refers to the correlation between cancer imaging features and gene expression. Imaging Genomics (Radiomics) can be used to create biomarkers that identify the genomics of a disease without the use of an invasive biopsy. The focus of this lab is detection of the 1p19q co-deletion biomarker using deep learning - specifically convolutional neural networks – using Keras and TensorFlow. What is remarkable about this research and lab is the novelty and promising results of utilizing deep learning to predict Radiomics.

Icon  level Intermediate 30 Credits 1 Hour 30 Minutes