Skip to content
Main menu hidden.

Deep Learning with Applications in Medical Imaging

  • Number of credits 7.5 credits
  • Level Master’s level
  • Starting Spring Term 2024

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.

Application and eligibility

Deep Learning with Applications in Medical Imaging, 7.5 credits

Det finns inga tidigare terminer för kursen Spring Term 2024 Det finns inga senare terminer för kursen


15 January 2024


2 June 2024

Study location



English (upon request)

Type of studies

Daytime, 25%

Required Knowledge

Univ: For access to the course requires 90 credits of completed studies in one of the main areas of computer science, physics, electronics, chemistry, mathematics or mathematical statistics are required, or 2 years of completed studies (120 credits). Of these credits, at least 7.5 credits are required in basic programming methodology in Python, C, and/or Matlab, at least 7.5 credits dealing with Data Structures and Algorithms, at least 7.5 credits dealing with Linear Algebra, at least 7.5 credits dealing with analysis with concepts such as derivatives and limit values, at least 7.5 credits dealing with mathematical statistics, or equivalent knowledge. English A/6 if the teacing language is english.




Academic credits 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 code



Please note: This application round is intended only for applicants within the EU/EEA and Switzerland. Online application service in Swedish will open 15 September 2023 at 13:00 CET. Application deadline is 16 October 2023. How to apply

Application and tuition fees

As a citizen of a country outside the European Union (EU), the European Economic Area (EEA) or Switzerland, you are required to pay application and tuition fees for studies at Umeå University.

Application fee

SEK 900

Tuition fee, first instalment

SEK 17,850

Total fee

SEK 17,850

Contact us

Please be aware that the University is a public authority and that what you write here can be included in an official document. Therefore, be careful if you are writing about sensitive or personal matters in this contact form. If you have such an enquiry, please call us instead. All data will be treated in accordance with the General Data Protection Regulation.

Course is given by
Radiation Sciences
Contactperson for the course is:
Tommy Löfstedt