Artificial intelligence has been successfully applied to bioimage understanding and achieved significative results in the last decade. Advances in imaging technologies have also allowed the acquisition of higher resolution images. That has increased not only the magnification at what images are captured, but the size of the acquired images as well. This comprises a challenge for deep learning inference in large-scale images, since these methods are commonly used in relatively small regions rather than whole images. This workshop presents techniques to scale-up inference of deep learning models to large-scale image data with help of Dask for parallelization in Python.

With cameras in everything from microscopes to telescopes to satellites, scientists produce image data in countless formats, shapes, sizes, and dimensions. Python provides a rich ecosystem of libraries to make sense of them. napari is a Python library for multidimensional image visualization, but it does double duty as a standalone application that can be easily extended with GUI tools for analysis, visualization, and annotation. In this tutorial, we'll start with the basics of image visualization and analysis in Python, then show how to extend the napari user interface to make analysis workflows as easy as pushing a button, and finally show how to share these extensions as plugins, which can be easily installed by users and collaborators. If you work with images (particularly multidimensional images), and especially if you work with scientists who may not be comfortable with Python, this tutorial might be for you!