AI & Companies Week Results - WAICF

Published on February 1, 2023 Updated on February 8, 2023

on the February 10, 2023

WAICF - Palais des Festivals, Cannes - 3IA BOOTH A13

Discover the results of the AI & Companies Week during the World AI Cannes Festival! PhD students will present their solutions after 1 week of work on a topic provided by an industrial partner.

The topics of this edition are the following:
  • Centre  d’Expérimentations  Pratiques  et  de  réception  de  l’Aéronautique  Navale  –  Marine  Nationale (Ministry of Defense)
    • Title: Automated analysis of physiological data from VR aerial simulation session
    • Abstract: From oculometric (eye movements) and cardiac data from a virtual reality headset the goal is to interpret the behaviour (under cognitive load, in cognitive overload or in nominal cognitive load) of the pilot performing the flight simulation or/and determine in which phase of flight he/she is (landing, take-off, simple  transit,  aerobatics).  For  this  purpose,  several  data  sets  will  be  provided. In addition to these anonymised data sets, the researchers will have access to the phases of flight corresponding to each data item. They will also know at each moment what state the pilot is in. The team will have access to the computer with its simulator and the VR headset (Reverb G2 Omnicept) during the Hackaton week. The team can be partially supported by CSIO flight person.
  • Caranx Medical is about the convergence between: Robotics, Imaging and AI.
    • Title: Processing and segmentation of femoral artery ultrasound images
    • Abstract: Automatic segmentation of medical images is an important step for deployment of surgical robots. For instance automatic detection of the location of the femoral artery from ultrasound images will allow the surgical robot to adapt in real time the trajectory of the probe to the patient’s anatomy. The goal of the project is based on segmented training images to propose an automated segmentation approach for new images. In order to increase the size the data set the team will also dispose of data from anatomic synthetic model thus also asking the question of domain adaptation of the model from synthetic data to true patients data.
  • inHEART is committed to delivering the world’s most sophisticated, AI-enabled, digital twin of the heart to advance the care of patients living with cardiac disease.
    • Title: Machine learning-based segmentation of medical images with partially labelled training data
    • Abstract: The success of machine learning (ML) approaches, especially artificial neural networks, relies heavily on the volume of available data and the quality of its labeling. Unfortunately, labeling data is a time and  money-consuming  task;  in  the  medical  field  it  often  requires  domain-specific  knowledge  for  the annotators. Is it possible to include partially or non-annotated data in a training dataset.
Joins us at booth A13 on 10 February at 10am!