6 EC
Semester 2, period 5
5354DLFM6Y
Artificial Intelligence has proven to be a great tool for helping radiologists and pathologists in diagnosing patients, and, ultimately, selecting the best possible patient-specific treatment. Computers can analyse digital images at an unmet speed and can detect patterns that are missed by medical experts. The development and application of artificial intelligence in medical imaging has sped up due to (1) widely available digital medical images, (2) freely available machine learning tools, and (3) high computing performance and GPU’s in particular. This combination has led to applications where computers are highly accurate in detecting patterns, and lesions to support diagnosis and prognosis. AI is used over the entire front of medical imaging, including designing optimal image acquisition schemes, acceleration of imaging, reconstruction of imaging, image enhancement, segmentation and classification.
In this course, we will focus on applying deep learning for digital medical image acquisition, processing and automatic analysis of images. The course will introduce the basic concepts of deep learning. Students will get hands-on experience in using the most common deep neural networks that are used in medical imaging, including convolutional neural networks such as U-net. Ultimately, the core of the course will focus on combining your technical background and knowledge about physics to allow you to apply and enhance deep learning approaches for medical imaging.
Understanding Deep Learning by Simon J.D. Prince: https://udlbook.github.io/udlbook/
Online on Canvas
3 extensive exercises
Python
Pytorch
Snelius and Collabnet
This course contains some self-study via Canvas, such that the students come well-prepared to the classes.
The classes will be interactive and activating.
We have 3 blocks, each block contains 1 week of predominantly theory and 1-2 weeks of hands-on programming of neural networks.
The final exercise will be presented as poster to your peers.
|
Activity |
Hours |
|
|
Classes |
14 |
(7 classes x 2 hours) |
| Practicals | 42 | (7 practicals x 4 + 7 x 2 hours) |
| Self study | 30 | |
|
Self programming |
80 |
|
|
Total |
168 |
(6 EC x 28 hours) |
Requirements concerning attendance (OER-B).
Additional requirements for this course:
Participation in the classes is expected and only 1 out of 7 classes can be missed.
Contact the coördinator if you are missing a class.
Participants must prepare the classes via self study.
| Item and weight | Details |
|
Final grade | |
|
35% Block 1: my first network | |
|
25% Block 2: CNN | |
|
20% Block 3: Image reconstruction | |
|
20% Poster Presentation |
The course consists of 3 blocks, each with an assignment.
Block 1: Introduction to neural networks (35%)
Block 2: Convolutional Neural Networks (25%)
Block 3: Deep learning for image reconstruction (20%+20%)
Each assignment can be done in pairs and counts towards your grade.
The assignments will be graded by TAs and feedback will be provided.
Furthermore, a Perusall assignment will be assigned to prepare for the lectures. Perusall will assign a grade to this assignment. The teachers will manually go through the comments and take meaningful remarks into account in the Perusall assessment.
At the end of the course, a mini-symposium will be held in which the students present the results of Block 3 to each other. The posters and discussion will be evaluated by the TAs and teachers.
The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl
| Weeknummer | Onderwerpen | Studiestof | Deadlines |
| 1 | Intro to deep learning | chapters 2-6 of book; canvas | |
| 2 | My first Numpy network | chapter 8-9; canvas; exercises | |
| 3 | My first PyTorch network | chapter 7; canvas; exercises | First exercise (35%) |
| 4 | Convolutional networks | chapter 10; canvas | |
| 5 | Convolutional networks | canvas | Second exercise (25%) |
| 6 | Image reconstruction | canvas | |
| 7 | Image reconstruction | canvas | |
| 8 | finishing up | Third exercise (20%); Poster presentation(20%) |
The students should be able to program in Python.
The students should have an understanding of basic calculus (derivatives, chain rule, etc.)
The students should have a basic understanding of medical imaging.
No prior experience in machine learning is expected/required.
--> This course is not meant for AI MSc students as it has a lot of overlap with their core courses on explaining the basics of deep learning. There is a seperate "AI for medical imaging" course for them.