PhD student
Yamil
VINDAS
CREATIS/UTC PhD Student
Additional informations
Home laboratory :
Créatis - Thesis directors (s) :Philippe Delachartre
- Thesis supervisor(s) :Emmanuel Roux et Blaise Kévin Guépié
- Start date of the thesis : 01/10/2020
- End date of the thesis : 30/09/2023
- Date of defence :
Weakly-supervised learning for emboli characterization with Transcranial Doppler (TCD) Monitoring.
The detection of emboli is a major topic as regards prevention of stroke. The cause of a stroke can be multiple, but in most cases, a Doppler monitoring of the middle cerebral artery (MCA) can help diagnosis and prevent stroke risk. The classification of emboli is essential to improve the reliability of diagnosis.
For more than 20 years, Atys Medical has been developing Doppler monitoring systems that can detect HITS (High Intensity Transient Signal) and classify them as emboli or artifacts (probe movement, eye blinking, voice artifact, etc...).
Atys latest device - unique in the world - is a Holter equipped with robotized probes (TCD-X), allowing long monitoring session, on ambulatory patients, thanks to automatic probe adjustments all along recording to assure optimal signal maintain.
One major topic to help doctors identify the cause of stroke is to focus on the nature of the emboli. During cardiac surgery or in stroke units, knowing that emboli is solid or gaseous may give essential clues to the doctor to better take in charge their patients. Also more precise information on the origin of emboli might be helpful.
The main aim of this project is to extract specific features for each emboli - based on a set of labeled and unlabeled Doppler archive- and use deep learning to classify events in an efficient way to give more accurate diagnosis and optimize time for data analysis
https://celya.universite-lyon.fr/members/yamil-vindas--261232.kjsp?RH=1524471175521
