Semester Projects

Term Projects, EURECOM, 2024

List of current and past semester projects

Spring 2024

Studying Generative Models in support of Medical Segmentation via Domain Adaptation - L. Nepote & G.A. Orlando
Analysis of Unsupervised Learning Techniques for Cerebrovascular Image Segmentation - M. Driss & H. Jad Al Aoud
Multimodal stress identification from videos, audio and physiological signals with missing modalities - B. Charlier

Advanced tools AI to denoise of multiparametric histology images - M. Pentassuglia
(in collaboration with AMKBiotech)

Quality control pipelines in medical image segmentation - Z. Thiry
Improving OOD generalization - N. Jamousi
Robust multi-task segmentation of the brain and the cerebrovascular tree from time-of-flight images - I. Pitsiorlas
(in collaboration with P. Arbelaez, Universidad de los Andes, CO)

Image style transfer for brain vessel segmentation from multi-modal MRI - D. Falcetta
Robust Image Segmentation - I. Pitsiorlas, H. Rechatin, K. Thornburg

Visualization tools for research portal on Deep learning for electroencephalography - J. Tan
(in collaboration with NeuroTechX)
Facial feature extraction from magnetic resonance mages for congenital disease prediction - P. Michel
(in collaboration with CHU Nice)
Image tools for understanding chronic inflammatory diseases from high frequency ultrasound images - S. Gosh
Joint-view action detection in indoor scenarios - S.H. Boyalla (in collaboration with Team Stars, INRIA)
First analysis and visualization tools for data collected from a tele-medicine station - M. Beurey
(in collaboration with Body O)

Exploring automatic diagnosis of COVID-19 through acoustic clues and ML - L. Cascioli & Y. Huang
(with M. Todisco & J. Patino)
Adversarial Attacks of Automated Lane Centering Systems in autonomous Vehicles -
(with M. Onen & O. Ermis, in collaboration with iABG)
Probabilistic Learning for Rethinopathy Detection - G. Abi Hanna
(in collaboration with iABG)
Multi-task Learning for Medical Image Segmentation - C. Chetta

Fine-grained car segmentation for 360° indoor studio shootings - T. Daneels
Data Science Tools for Wearable Devices - G. Abi Hanna & P. Volpe
Waste sorting error detection - T. Diaconu
Exploring automatic diagnosis of COVID-19 through acoustic clues and ML - L. Cascioli & Y. Huang (with M. Todisco & J. Patino)
Adversarial Learning against Autonomous Vehicles - M. Njeh
(with M Onen & O Ermis, in collaboration with iABG)

Exploring learning schemes to deal with constrained resources - R. Schiavone
Studying GANs for anomaly detection - F. Galati
Segmenting 3D medical images using 2D images as training data - G. Di Giacomo & V. Marconetto
(in collaboration with University College London)

Needles in a haystack or vessels in a brain: Classifying small objects in large datasets - P. Mathur & V. Dang Ngoc