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.
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
- Lecture Slides
- Old Lecture Video: Part 1, Part 2
- Lecture Slides
- Old Lecture Handout as PDF
- Lecture Video: Part 1, Part 2
- Lecture Slides
- Old Lecture Handout as Old PDF
- Lecture Video: Part 1, Part 2
- Lecture Slides
- Old Lecture Handout as Old PDF
- Lecture Video: Part 1, Part 2
- Lecture Slides
- Old Lecture Handout
- Lecture Video: Part 1, Part 2
- Lecture Slides
- Old Lecture Handout as PDF
- Lecture Video: Part 1, Part 2
- Lecture Slides
- Lecture Handout as old PDF
- Lecture Video: Part 1, Part 2, Part 3
- Lecture Slides
- Old Old Lecture Handout as PDF
- Lecture Video: Part 1
- Lecture Slides
- Old Lecture Handout as PDF
- Lecture Video: Part 1 and Part 2
- High Content Screening Slides - Michael Prummer / Nexus / Roche
1st June - Guest Lecture - Big Aerial Images with Deep Learning and More Advanced Approaches (J. Montoya)
- Roads from Aerial Images Slides - Javier Montoya / Computer Vision / ScopeM
- Deep Learning Slides (A. Lucchi)
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)
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.
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.
- For all exercises it is important to take the starting data
- Starting Data
- The KNIME or workflow based exercises are here
- KNIME Exercises
- You can get started on Kaggle (no installation required just register)
- Online Dataset
- Online Kernel for Exercises 1-3 and Exercise 4
- Additionally there is an competition on Image Enhancement
- For students experienced in Python there is an Jupyter notebook Exercise1-3, Exercise or download and how to get Jupyter on the D61 Machines
- An older version of the exercises in Matlab are available here
- MNIST/Digit Recognizer Dataset (www.kaggle.com/c/digit-recognizer)
- Download data
- KNIME Exercises
- Old IPython Exercises, and Old IPython Solutions/Advanced but these are still incomplete
- Kernel for Ultrasound Segmentation - Exercises
- Kernel for Superpixels on PETCT
- Kernel for K-Means on Temporal/Video Data
- Advanced Kernel Predicting Malignancy using Superpixels
- Multispectral / Hyperspectral Data
- KNIME Exercises
- Kaggle EM Cell Segmentation Intro and Jupyter Notebook
- Kaggle MNIST Shape Analysis
- Old Creating Meshes/STL Models
- KNIME Exercises
- Kaggle Street Network
- Kaggle Electron Microscopy Segmentation
- Paraview Curvature
- Old IPython Notebook (Under development)
- KNIME Exercises
- C. Elegans Dataset on Kaggle R Notebook or Python Notebook
- Lung Segmentation [https://www.kaggle.com/kmader/dsb-lung-segmentation-algorithm/notebook](Rule-based Image Processing) and Simple Neural Network
- High Content Screening with C. Elegans
- Goal is looking at what metrics accurately indicate living or dead worms and building a simple predictive model
- Kaggle Overview
- Shape Analysis
- Processing in R
1st June - Guest Lecture - Big Aerial Images with Deep Learning and More Advanced Approaches (J. Montoya)
- Machine Learning Aerial Images
- KNIME Exercises
- KNIME Workflow
- IPython Notebook
- Deep Learning with Aerial Images
- Python Data
- IPython Notebook
- 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
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.
- Overview of possible projects
- Here you signup for your project with team members and a short title and description
- Course Wiki (For Questions and Answers, discussions etc)
- Main Page
- Performance Computing Courses
- High Performance Computing for Science and Engineering (HPCSE) I
- Introduction to GPU Programming
- Programming Massively Parallel Processors with CUDA
- Reprodudible Research Courses
- Course and Tools in R
- Coursera Course