Deep Learning with Applications in Medical Imaging
7.5 credits
About the course
This course covers deep convolutional neural networks (CNNs) for computer vision, with applications in medical image analysis. The course provides an introduction to fundamental concepts in machine learning, describes neural networks and the field of deep learning, and goes into detail about deep CNNs. The course describes the different parts that are used when building deep CNNs, such as filters, activation functions, loss functions; regularization techniques such as e.g. batch normalization and dropout; explains several of the different non-linear optimization algorithms that are used when training the networks, such as stochastic gradient descent, Adam, etc.; and describes popular network architectures, such as e.g. the U-Net, ResNet, and DenseNet, and discusses their pros and cons. The course also covers generative models, such as variational autoencoders (VAE) and generative adversarial networks (GANs).
Students in this course will learn to implement and train modern network architectures and deep learning methods, and apply these to large image datasets with medical and other images.
