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2020 – today
- 2024
[j2]Refka Hanachi
, Akrem Sellami, Imed Riadh Farah, Mauro Dalla Mura:
Graph feature fusion driven by deep autoencoder for advanced hyperspectral image unmixing. Knowl. Based Syst. 299: 112087 (2024)- [j1]Refka Hanachi, Akrem Sellami, Imed Riadh Farah, Mauro Dalla Mura:
Multi-view graph representation learning for hyperspectral image classification with spectral-spatial graph neural networks. Neural Comput. Appl. 36(7): 3737-3759 (2024) - [c6]Refka Hanachi, Akrem Sellami, Imed Riadh Farah, Mauro Dalla Mura:
Advanced graph deep learning for High-dimensional image analysis: challenges and opportunities. ATSIP 2024: 488-493 - 2023
- [c5]Refka Hanachi, Akrem Sellami, Imed Riadh Farah, Mauro Dalla Mura:
DNGAE: Deep Neighborhood Graph Autoencoder for Robust Blind Hyperspectral Unmixing. ICCCI 2023: 84-96 - 2022
- [c4]Mariem Ayed, Refka Hanachi, Akrem Sellami, Imed Riadh Farah, Mauro Dalla Mura:
A deep learning approach based on morphological profiles for Hyperspectral Image unmixing. ATSIP 2022: 1-6 - [c3]Refka Hanachi, Akrem Sellami, Imed Riadh Farah, Mauro Dalla Mura:
Multi Spectral-Spatial Gabor Feature Fusion Based On End-To-End Deep Learning For Hyperspectral Image Classification. WHISPERS 2022: 1-6 - 2021
- [c2]Refka Hanachi, Akrem Sellami, Imed Riadh Farah:
BS-GAENets: Brain-Spatial Feature Learning Via a Graph Deep Autoencoder for Multi-modal Neuroimaging Analysis. VISIGRAPP (Revised Selected Papers) 2021: 303-327 - [c1]Refka Hanachi, Akrem Sellami, Imed Riadh Farah:
Interpretation of Human Behavior from Multi-modal Brain MRI Images based on Graph Deep Neural Networks and Attention Mechanism. VISIGRAPP (4: VISAPP) 2021: 56-66
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