BS-GAENets: Brain-Spatial Feature Learning Via a Graph Deep Autoencoder for Multi-modal Neuroimaging Analysis. - Refka Hanachi; Akrem Sellami; Imed Riadh Farah (VISIGRAPP (Revised Selected Papers), 2021)

Interpretation of Human Behavior from Multi-modal Brain MRI Images based on Graph Deep Neural Networks and Attention Mechanism. - Refka Hanachi; Akrem Sellami; Imed Riadh Farah (VISIGRAPP (4: VISAPP), 2021)

A deep learning approach based on morphological profiles for Hyperspectral Image unmixing. - Mariem Ayed; Refka Hanachi; Akrem Sellami; Imed Riadh Farah; Mauro Dalla Mura (ATSIP, 2022)

Multi Spectral-Spatial Gabor Feature Fusion Based On End-To-End Deep Learning For Hyperspectral Image Classification. - Refka Hanachi; Akrem Sellami; Imed Riadh Farah; Mauro Dalla Mura (WHISPERS, 2022)

DNGAE: Deep Neighborhood Graph Autoencoder for Robust Blind Hyperspectral Unmixing. - Refka Hanachi; Akrem Sellami; Imed Riadh Farah; Mauro Dalla Mura (ICCCI, 2023)

Advanced graph deep learning for High-dimensional image analysis: challenges and opportunities. - Refka Hanachi; Akrem Sellami; Imed Riadh Farah; Mauro Dalla Mura (ATSIP, 2024)

Graph feature fusion driven by deep autoencoder for advanced hyperspectral image unmixing. - Refka Hanachi; Akrem Sellami; Imed Riadh Farah; Mauro Dalla Mura (Knowl. Based Syst., 2024)

Multi-view graph representation learning for hyperspectral image classification with spectral-spatial graph neural networks. - Refka Hanachi; Akrem Sellami; Imed Riadh Farah; Mauro Dalla Mura (Neural Comput. Appl., 2024)