Diego Ulisse Pizzagalli: Image driven systems immunology via semi-supervised machine learning and in-vivo microscopy
17 January 2019 - 17 January 2019
IDSIA, Galleria 1
This talk describes the combination of machine learning with
microscopy techniques for investigating the mechanisms of the immune
system. After giving an overview about the applications of machine
learning to in-vivo imaging, the capabilities of a graph-based,
semi-supervised clustering algorithm will be presented.
More in details, the immune system involves a complex network of
cellular interactions. This network can be described as a system whose
output can be either protective (i.e. from pathogens and tumors) or
pathogenic (i.e. leading to autoimmune diseases). In-vivo video
microscopy (IVM) is a recently developed method to investigate the
behavior of the immune system in living animals. IVM acquires 4D
videos capturing the migration of cells which correlates to their
spatiotemporal interaction patterns. However, automatic classical
automatic analysis methods for this type of data require cell
segmentation and tracking which are challenging due to the high
plasticity, lack of textures and frequent contacts between cells. To
this end, we present a semi-supervised clustering algorithm for
segmentation and tracking by grouping voxels according with a
trainable grouping criterion. Moreover, we present novel analysis
methods that do not require segmentation nor tracking.

The speaker

Diego Ulisse Pizzagalli
PhD Candidate and Teaching Assistant
Institute for Research in Biomedicine - Bellinzona
Institute of Computational Science, USI