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The material for the Quantitative Big Imaging Course at ETHZ for Spring Semester 2017

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Quantitative Big Imaging Course 2017

Here are the lectures, exercises, and additional course materials corresponding to the spring semester 2016 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.

Slack

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

Lectures

23th February - Introduction and Workflows

2rd March - Image Enhancement (A. Kaestner)

9rd March - Tutorial: Python, Notebooks and Scikit

16th March - Basic Segmentation, Discrete Binary Structures

23rd March - Advanced Segmentation

30th March - Analyzing Single Objects

6th April - Analyzing Complex Objects

13th April - Many Objects and Distributions

27st April - Statistics, Prediction, and Reproducibility

4th May - Dynamic Experiments

11th May - Scaling Up / Big Data

18th May - Guest Lecture - High Content Screening (M. Prummer) / Project Presentations

1st June - Guest Lecture - Big Aerial Images with Deep Learning and More Advanced Approaches (J. Montoya)

Old Guest Lectures

Exercises

General Information

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 last year (available on: kmader.github.io/Quantitative-Big-Imaging-2015/) are done entirely in ImageJ and Matlab for students who would prefer to stay in those environments (not recommended)

Assistance

The exercises will be supported by Yannis Vogiatzis, Kevin Mader, and Christian Dietz. There will be office hours in ETZ H75 on Thursdays between 14-15 or by appointment.

Online Tools

The exercises will be available on Kaggle as 'Datasets' and we will be trying binder as well which is well suited for Open Source reproducible science.

Specific Assignments

23rd February - Introduction and Workflows (Christian Dietz, Intro to KNIME for Image Processing)

2nd March - Image Enhancement (A. Kaestner)

  • For all exercises it is important to take the starting data
  • Starting Data

KNIME

Python

Matlab (just for this exercise)

  • An older version of the exercises in Matlab are available here

9th March - Tutorial: Python, Notebooks and Scikit

Contest Data

Python Notebooks

Kaggle Kernels

Video Tutorials

16th March - Basic Segmentation, Discrete Binary Structures

Kaggle

23rd March - Advanced Segmentation

Kaggle

30th March - Analyzing Single Objects

6th April - Analyzing Complex Objects

13th April - Many Objects and Distributions

27th April - Statistics, Prediction, and Reproducibility

4th May - Dynamic Experiments

11th May - Scaling Up / Big Data

18th May - Guest Lecture - High Content Screening (M. Prummer) / Project Presentations

1st June - Guest Lecture - Big Aerial Images with Deep Learning and More Advanced Approaches (J. Montoya)

Old Guest Lecture Exercises

Feedback (as much as possible)

  • Create an issue (on the group site that everyone can see and respond to, requires a Github account), issues from last year
  • Provide anonymous feedback on the course here
  • Or send direct email (slightly less anonymous feedback) to Kevin

Final Examination

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.

Projects

  • Overview of possible projects
  • Here you signup for your project with team members and a short title and description

Other Material