Convolutional neural networks (CNNs) have proven to be just as effective in visual recognition tasks involving non-visible image types as regular RGB camera imagery. One important application of these capabilities is medical image analysis, where we wish to detect features indicative of medical conditions and use them to infer patient status. In addition to processing non-visible imagery, such as CT scans and MRI, these applications often require us to process higher dimensionality imagery that may be volumetric and have a temporal component. In this lab you will use the deep learning framework MXNet to train a CNN to infer the volume of the left ventricle of the human heart from a time-series of volumetric MRI data. You will learn how to extend the canonical 2D CNN to be applied to this more complex data and how to directly predict the ventricle volume rather than generating an image classification. In addition to the standard Python API, you will also see how to use MXNet through R, which is an important data science platform in the medical research community. Prerequisites: Basic knowledge of CNNs. This lab utilizes GPU resources in the cloud, you are required to bring your own laptop.