Computational imaging, modelling and AI in biomedicine
Postgraduate course
- ECTS credits
- 10
- Teaching semesters
- Spring
- Course code
- BMED365
- Number of semesters
- 1
- Teaching language
- NorwegianEnglishAll study material will be in English
- Resources
- Schedule
Course description
Objectives and Content
Learning Outcomes
Upon completion of the course, the student must have the following learning outcomes defined in terms of knowledge, skills, and general competence:
Knowledge
The student ...Has broad knowledge of the terms "multiscale" and "multiparametric biomedical imaging",
"computational modeling", "big data", "network analysis", "machine learning", "deep learning", and "generative AI" (incl. large language models) and was able to relate these terms to examples from biomedicine and personalized and precision medicine.
Skills
The student ...Can find and use a selection of modern software tools for data analysis, visualization, reporting, and generative AI, e.g. explorative data analysis, figure and graphics production with Jupyter notebooks, and efficient prompting of large language models such as ChatGPT. Can communicate about selected methods, software packages, and libraries where these methods have been implemented and explain their relevance in biomedical research and medicine.
General competence
The student ...Recognizes the importance of mathematical models and computational approaches, as well as large language models, for the analysis and understanding of complex systems and disease processes. Appreciate the need for interdisciplinary collaboration in biomedicine of the future. Ethical and regulatory aspects of biomedical AI. Can analyze how scientific collaboration in the form of "open science", sharing of data, and "reproducible research" can move science forward.
ECTS Credits
Level of Study
Semester of Instruction
Required Previous Knowledge
Recommended Previous Knowledge
Credit Reduction due to Course Overlap
Access to the Course
Students admitted to a Master's program at the Faculty of Medicine or the Faculty of Mathematics and Natural Sciences at UiB (or another university) and students admitted to the engineering studies at HVL (or another university, e.g. Erasmus student). Qualified students from outside UiB will receive guest student status upon admission to the course.
There is room for max 20 students, with 10 places reserved for master's students in biomedicine.
Teaching and learning methods
The teaching style is oriented towards "blended learning" and "flipped classroom":
The course is divided into two blocks. In the first block there will be two days of introductory and motivational lectures, including demonstrations.There wil be e-learning/lab modules on a GitHub repository (before, during, and available after the course) addressing the learning outcomes for the course. A submission related to a specific topic within biomedicine, chosen from among a small selection of pre-defined projects, is mandatory. This will be organized as projects in small teams, where collaboration between students majoring in different topics in their Bachelor e.g. biology and informatics, respectively ("tandem"), is sought. This interdisciplinary team project must be presented orally at one of the last meetings in the first block. The second block will focus on computational biomedical imaging (MRI, IMC, ...) and modelling. Between the first and second block, the students will work on their own personal project, defined by the student henself within the scope of the course. This project must be submitted before the end of block two and will be presented to the rest of the students as a "speed posters". There will be a total of six "labs"/"meet-ups" with teachers and teaching assistents. Final digital exam. The course will assume the students have their own laptop (or borrow one).
Compulsory Assignments and Attendance
One compulsory team project participation, submission, and group presentation, followed by an individual project with a "speed-poster" presentation, partly with a peer assessment component. Compulsory activities are registered by the course supervisor and must be passed before the final exam.
Forms of Assessment
Team project and group presentation in block one, and submission of a personal project and presentation of corresponding "speed poster" in block two must be approved.
Final digital home exam (2 hours) with quiz and MCQ with a pass / no-pass.
Grading Scale
Assessment Semester
Reading List
The literature list and study material (on GitHub) will be ready by 01.07 for the autumn semester and 01.12 for the spring semester.