Connectionists: Multiple internships in Medical Imaging and Federated Learning at IHU Strasbourg

[Alex Karargyris]

Today’s operating room (OR) has been transformed into a convoluted setting
of machines, surgeons, nurses, and patients. Large amounts of data are
generated just in a single operation. These data are temporal and
multimodal (e.g. endoscopy videos, radiological, physiological, human
movement, etc.) providing a rich context of the operation. In this project,
the intern will research self-supervision , weak supervision, multimodal
fusion methods to segment, classify or analyze medical images such as CT
and MRI scans.

Federated Learning (FL) is a new technique that has been proposed to
circumvent concerns related to privacy and data ownership during machine
learning. Our lab is developing infrastructure for rich temporal
multi-modal data in the operating room, and we are developing methods that
can help us improve efficiency of FL algorithms. The intern will have the
opportunity to work with a group of scientists and clinicians and research
novel algorithms related to privacy preserving approaches, noisy data and
self-supervision in FL settings

Preference is given to candidates registered to EU universities.

For more information please contact Alexandros Karargyris at
alexandros.karargyris (at) ihu-strasbourg.eu

More information here: http://camma.u-strasbg.fr/opportunities
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