Sparse Coding of Dynamic Point Clouds for Scene Analysis and Reconstruction (SPYDER)

Analysis of Multi-Sensor Remote Sensing Data with Advanced Deep Learning Models

Automatic, small-scale sea-floor characterisation from high-resolution sonar data

Data Efficient Deep Learning Models for Biomedical Image Segmentation

Deep Learning Methods for Crack Detection and Image Restoration with Application to Digitized Paintings

Autoencoder-Based Image Dimensionality Reduction Methods

Doctoral project Andrei Sechelea

Doctoral project Attila Fésüs

Doctoral project Hrvoje Leventić

Doctoral project Laurens Meeus

Doctoral project Lingchen Gu

Doctoral project Marko Panic

Doctoral project Shaoguang Huang

Doctoral project Ting Zhao

Doctoral project Xian Li

Graph neural network models for 3D segmentation and shape analysis of anatomy images

Model-aware deep learning in multimodal image analysis

Optimization of Deep Learning Algorithms for the Cardiovascular System Image Processing Using Residual Units

ARTDETECT Project 3. Craquelure studies: telling the difference between artificial and natural signs of ageing

Doctoral project Andres Frias Velazquez

Doctoral project Danilo Babin

Doctoral project Funing Bai

Doctoral project Jorge Niño Castañeda

Doctoral project Keyvan Kasiri

Doctoral project Nicolas Vercheval

Doctoral project Xiaoxia Qu

Self-supervised deep learning models for multimodal medical image analysis

Alliance Research Group: International Big Data Analytics Lab

BALaTAI: Belgian Art Links & Tools for Artificial Intelligence

Big Heterogeneous Data Sensing and Processing in Computer Vision

Genetic predictors of joint shape and cartilage mechanics

Dictionary Learning and Distributed Inference for the Processing of Large-scale Heterogeneous Image Data (DOLPHIN)

Advanced quantitative tomographic image reconstruction

Imprecise continuous-time Markov chains

Model-aware deep learning for inverse imaging problems

Massive Data Mining for Remote Sensing Imagery 

Towards Reliable Land Cover Change Detection from Multi-Sensor Data (REALISE)

Improving QMRI By Realizing trustworthy integration of AI in Neuro-imaging

statisticl modeling of visual patterns wiht applications to digital painting analysis