ImageXD 2017, The University of Washington eScience Institute, March 29th-31st
The workshop will follow the three-day format of the workshop from last year with each day focused on a different activity:
On Day 1 (Wednesday, March 29), four tutorials are planned:
NumPy, SciPy, and Matplotlib, an introduction to numerical analysis with Python.
This tutorial is aimed at those familiar with Python, but who have not used NumPy, SciPy, and Matplotlib extensively before.
TensorFlow, neural networks in Python.
In this tutorial we learn how to train a neural network using the TensorFlow library, and how to then apply it to real-world data.
scikit-image, image processing in Python.
We introduce the scikit-image library, demonstrate its various capabilities, and do several hands-on exercizes to practice its use.
ImageJ, image processing in Java.
We explore ImageJ and its SciJava interface with examples in Python and Clojure.
On Day 2 (Thursday, March 30), selected speakers will share their expertise.
9:45 - 10:15 Talita Perciano, Lawrence Berkeley National LaboratorY: Image Segmentation Across Domains using Parallel Markov Random Field Technique
10:30 - 12 Breakout sessions
12:00 - 1 : Lunch break
1 - 1:30 Kyle Harrington, University of Idaho: Biological Image Analysis using the ImageJ/Fiji Software Ecosystem
1:30 - 2:00 Ira Kemelmacher-Shlizerman, The University of Washington: Modeling people from historical photos
2:00 - 2:30 Claire McQuin, Broad Institute: Developing High Content Image Analysis Software for Biologists
2:30 - 3 Lydia Ng, Allen Institute for Brain Science: Delivering data, tools and knowledge on brain cell types, connections and activity
3:15 - 3:45 Mario Juric, The University of Washington: Object Detection in Astronomy: Current Practice & Challenges Going Forward
4 - 4:45 : Breakout sessions
4:45 Wrap-up for the day
5 : reception
On Day 3 (Friday, March 31), we will get together to work in teams to develop ideas opportunities in data, software, algorithms, and learning. This open format will allow participants to learn from each other, work together and explore concepts that cross domain boundaries.