The material for the Quantitative Big Imaging course at ETHZ for the Spring Semester 2019
Here are the lectures, exercises, and additional course materials corresponding to the spring semester 2019 course at ETH Zurich, 227-0966-00L: Quantitative Big Imaging.
The lectures have been prepared and given by Kevin Mader and associated guest lecturers. Please note the Lecture Slides and PDF do not contain source code, this is only available in the handout file. Some of the lectures will be recorded and placed on YouTube on the QBI Playlist. The lectures are meant to be followed in chronological order and each lecture has a corresponding hands-on exercise. The entire lecture set is available as a single PDF file available in the releases section
The course is designed with both advanced undergraduate and graduate level students in mind. Ideally students will have some familiarity with basic manipulation and programming in languages like Python (Matlab or R are also reasonable starting points). Much of the material is available as visual workflows in a tool called KNIME, although these are less up to date than the Python material. Interested students who are worried about their skill level in this regard are encouraged to contact Kevin Mader directly (mader@biomed.ee.ethz.ch).
For communicating, discussions, asking questions, and everything, we will be trying out Slack this year. You can sign up under the following link. It isn’t mandatory, but it seems to be an effective way to engage collaboratively How scientists use slack
Part 1: Slides (static) Lecture Handout
Part 2: Slides (static) Lecture Handout
Part 1: Slides (static) Handout
Part 2: Slides (static) Handout
The exercises are based on the lectures and take place in the same room after the lecture completes. The exercises are designed to offer a tiered level of understanding based on the background of the student. We will (for most lectures) take advantage of an open-source tool called KNIME (www.knime.org), with example workflows here (https://www.knime.org/example-workflows). The basic exercises will require adding blocks in a workflow and adjusting parameters, while more advanced students will be able to write their own snippets, blocks or plugins to accomplish more complex tasks easily. The exercises from two years ago (available here are done entirely in ImageJ and Matlab for students who would prefer to stay in those environments (not recommended)
If you use colab, kaggle or mybinder you won’t need python on your own machine but if you want to set it up in the same way the class has you can follow the instructions shown in the video here and below
Downloads/Quantitative-Big-Imaging-2019-master/binder
)conda env create -f environment.yml
conda activate qbi2019
or activate qbi2019
cd ..
jupyter notebook
The exercises will be supported by Amogha Pandeshwar and Kevin Mader. There will be office hours in ETZ H75 on Thursdays between 14-15 or by appointment.
The exercises will be available on Kaggle as ‘Datasets’ and we will be using mybinder as stated above.
The final examination (as originally stated in the course material) will be a 30 minute oral exam covering the material of the course and its applications to real systems. For students who present a project, they will have the option to use their project for some of the real systems related questions (provided they have sent their slides to Kevin after the presentation and bring a printed out copy to the exam including several image slices if not already in the slides). The exam will cover all the lecture material from Image Enhancement to Scaling Up (the guest lecture will not be covered). Several example questions (not exhaustive) have been collected which might be helpful for preparation.
The course, slides and exercises are primarily done using Python 3.6 and Jupyter Notebook 5.5. The binder/repo2docker-compatible environment](https://github.com/jupyter/repo2docker) can be found at binder/environment.yml. A full copy of the environment at the time the class was given is available in the wiki file. As many of these packages are frequently updated we have also made a copy of the docker image produced by repo2docker uploaded to Docker Hub at https://hub.docker.com/r/kmader/qbi2018/
The packages which are required for all lectures
For machine learning and big data lectures a few additional packages are required
For the image registration lecture and medical image data
Javier Montoya / Computer Vision / ScopeM
Presented by Aurelien Lucchi in Data Analytics Lab in D-INFK at ETHZ