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.
The course has two modules: Theory and method, 5.5 ECTS credits Practical assignments, 2.0 ECTS credits
Convolutional Neural Networks with Applications in Medical Image Analysis, 7.5 credits
Spring Term 2023
16 January 2023
4 June 2023
English (upon request)
Type of studies
To be admitted you must have 90 ECTS credits in one of the main areas of computer science, physics, electronics, chemistry, mathematics or mathematical statistics, or two years of completed studies (120 ECTS credits). To be admitted to the course, the credits above must include at least 7.5 ECTS credits within Programming methodology (e.g. 5DV104, 5DV157, 5DV158, 5DV176, or 5DV177), at least 7.5 ECTS credits within Data structures and algorithms (e.g. 5DV149, 5DV150, or 5DV169), at least 7.5 ECTS credits within Linear algebra (e.g. 5MA019 or 5MA160), at least 7.5 ECTS credits within Calculus (e.g. 5MA009 or 5MA153), at least 7.5 ECTS credits within Mathematical statistics (e.g. 5MS005, 5MS045, 5MS043, 5MS068, or 5MS069), or equivalent knowledge. Proficiency in English equivalent to Swedish upper secondary course English A/5. Where the language of instruction is Swedish, applicants must prove proficiency in Swedish to the level required for basic eligibility for higher studies
Applicants in some programs at Umeå University have guaranteed admission to this course. The number of places for a single course may therefore be limited.
Application deadline was
17 October 2022.
Please note: This second application round is intended only for EU/EEA/Swiss citizens.