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Working in a hands-on learning environment, led by our 3D Medical Image Analysis with PyTorch expert instructor, students will learn about and explore: How to load and process imaging data for deep learning applications.How to build a convolutional neural network.How to train a neural network for a regression task.How to evaluate the predictions of your neural network.How to handle and visualize medical imaging data.

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Unlimited Duration

Last Updated

July 29, 2021

Students Enrolled

20

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Certification

In this course, you will be filling the role of a machine learning engineer/researcher at a healthcare technology company specializing in medical imaging applications. Your team wants to process and analyze magnetic resonance (MR) images of the brain. An MR imaging system is a flexible device that can create multiple types of images based on what a physician wants to see, but not all types of images are acquired in every scan due to time constraints. Your current processing and analysis algorithms require two types of MR images, but a new set of customer data only has one of those types. However, you have access to a fairly large, preprocessed dataset of paired examples of the two types of MR images, and you decide that deep learning would best perform this type of image transformation task.

Course Curriculum

    • Training and Validation Data Setup 00:00:00
    • Volumetric Data 00:00:00
    • Submit Your Work 00:00:00
    • Datasets and Transforms 00:00:00
    • Submit Your Work 00:00:00
    • Create Your Neural Network 00:00:00
    • Using Convolutions to Generalize 00:00:00
    • Submit Your Work 00:00:00
    • Train the Network 00:00:00
    • The Mechanics of Learning 00:00:00
    • Submit Your Work 00:00:00
    • Evaluate the Results 00:00:00
    • Structuring Deep Learning Projects and Hyperparameters tuning 00:00:00
    • Submit Your Work 00:00:00

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