If you have a subscription to ChatGPT Plus, you can also try out the Medical AI Assistant (UiBmed - ELMED219 & BMED365) and see if you can get it to answer some of your questions.
The course is offered by the Department of Biomedicine in collaboration the Medical AI group at Mohn Medical Imaging and Visualization Center (MMIV).
The objective and content of the course address: The computational mindset, imaging, modeling, machine learning, and AI in future biomedicine - ethical and regulatory aspects of AI. The course is a guided "journey" with a hands-on component through selected computational modeling techniques within biomedical and medical applications. Examples, demonstrations, and tasks will be related to in vivo imaging (MRI) and segmentation, imaging mass cytometry (IMC), biomarkers and prediction, network analysis ("patient similarity networks"), multimodal data, as well as large language models ("foundation models") within medicine and biology. Throughout the course, students will use principles and modern tools for data analysis, machine learning, and generative AI (e.g. ChatGPT) within biomedical applications. This will give the students an introduction to Python and Jupyter notebooks, use of the "cloud" for access to open data, calculations, and knowledge, as well as insight into and rationale for "open science" and "reproducible research". All course material is openly available on this GitHub repository. (See also ELMED219)
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This repository contains most of the course material. Students enrolled in the course will also find some practical information at MittUiB.
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On your own, you are encouraged to spend approximately four hours completing at least one DataCamp course (remember to use the link on MittUiB for free access to DataCamp). Which DataCamp course you're encouraged to do depends on your previous programming experience:
- No Python programming experience? Complete the course Introduction to Python
- Know some Python, but no machine learning? Complete the course Supervised Learning with scikit-learn
- Know the fundamentals of machine learning in Python? Can you pass the Machine Learning Fundamentals Assessment? Complete the course Biomedical Image Analysis in Python
- No Python programming experience? Complete the course Introduction to Python
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For academic questions about the course, contact course coordinator Arvid Lundervold (UiB).
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For practical or administrative inquiries, contact the Studies Section at the Department of Biomedicine at studie.biomed@uib.no
The content for the course is offered with a CC BY-SA 4.0 license unless otherwise stated.
| TIME | ACTIVITY |
|---|---|
| Week 1 Tue, Jan 2 |
|
| On your own | Get an overview of the course; installation of software and/or test out Google Colab |
| Follow the instructions at setup.md and MittUiB | |
| Week 1 Wed, Jan 3 |
|
| 10:15-14:00 BB Hist 1 |
Information About the course Motivation lectures - Computational medicine - Medical AI |
| Arvid Lundervold | |
| Week 1 Thu, Jan 4 |
|
| 14:15-15:00 BB Hist 1 |
SW-installation Tools, [teams] and project |
| Arvid Lundervold / Ben Bjørsvik | |
| 15:15-16:00 BB Hist 1 |
LAB 0: Introduction to theory and tools for machine learning |
| Ben Bjørsvik / Arvid Lundervold | |
| Week 1 Fri, Jan 5 |
|
| 10:15-11:00 BB Hist 1 |
LAB 0: Introduction to theory and tools for machine learning cont. |
| Ben Bjørsvik / Arvid Lundervold | |
| 11:15-15:00 BB Hist 1 |
LAB 0: Network science and patient similarity networks (PSN) |
| LAB 1: Brain imaging (mpMRI) in glioma | |
| Arvid Lundervold | |
| Week 2 Tue, Jan 9 |
|
| 09:15-13:00 BB Hist 1 |
Crash-course in Python programming |
| Ben Bjørsvik | |
| Week 2 Fri, Jan 12 |
|
| 08:15-13:00 BB Hist 1 |
Lab 2: Deep learning |
| Arvid Lundervold | |
| Week 3 Team project |
Joint with ELMED219 - Working on project in interdisciplinary teams |
| Week 3 Tue, Jan 16 |
|
| 09:15-12:00 BB Hist 1 |
Lab 3: Generative AI / Large Language Models |
| Arvid Lundervold | |
| 13:15-16:00 BB Hist 1 |
Meet-up for team project brainstorming and coaching |
| Arvid Lundervold / Ben Bjørsvik | |
| Week 4 Wed, Jan 24 |
|
| 08:15-10:00 BB Hist 1 |
Project presentations by team (jointly with ELMED219) |
| Arvid Lundervold / Ben Bjørsvik | |
| Week 4 Fri, Jan 26 |
|
| 08:15-10:00 BB Hist 1 |
Block 2 intro + Imaging (IMC and mpMRI) - motivation, demonstration |
| Arvid Lundervold | |
| 16:00 | Deadline for the Team Project Report - joint with ELMED219 (hand in via MittUiB) |
| Week 5 | |
| Jan 29 - Feb 02 | Working individually on home project |
| Week 6 | |
| Feb 05 - Feb 09 | Working individually on home project |
| Week 6 Sat 10 |
|
| 23:59 | Deadline for the Home Project Poster (hand in via MittUiB) |
| Week 7 Mon, Feb 12 |
|
| 08:15-10:00 BB Hist 1 |
Lab 4: Computational imaging |
| Arvid Lundervold | |
| Week 7 Wed, Feb 14 |
|
| 08:15-12:00 BB Hist 1 |
Presentation of individual home project as speed-poster |
| Organized as "leave-one-out": each student present, the others comment | |
| Week 7 Fri, Feb 16 |
|
| 08:15-10:00 BB Hist 1 |
Modeling (compartment models, and more) - motivation, demonstration |
| Arvid Lundervold | |
| Week 8 Mon, Feb 19 |
|
| 08:15-10:00 | Lab 5: Computational modeling |
| Arvid Lundervold | |
| Week 8 Fri, Feb 23 |
|
| 08:15-10:00 BB Hist 1 |
Summing up, reflections, future perspectives AI in biomedicine, finale |
| Arvid Lundervold | |
| Mon, Feb 26 | |
| Home exam: Duration: 2 hours; Assignment is handed out: 26.02.2024, 13:00; Submission deadline: 26.02.2024, 15:00; Examination system: Inspera Digital exam |
"In Vivo Imaging and Physiological Modelling"
| Year | Link |
|---|---|
| 2021 | https://github.com/computational-medicine/BMED360-2021 |
| 2020 | https://github.com/computational-medicine/BMED360-2020 |