Training Deep Learning Models with Hybrid Datasets for Robust Automatic Target Detection on real SAR images

Autor: Camus, Benjamin, Voillemin, Théo, Barbu, Corentin Le, Louvigné, Jean-Christophe, Belloni, Carole, Vallée, Emmanuel

In this work, we propose to tackle several challenges hindering the development of Automatic Target Detection (ATD) algorithms for ground targets in SAR images. To address the lack of representative training data, we propose a Deep Learning approach

Externí odkaz: http://arxiv.org/abs/2405.09588

Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank

Autor: Alfaro-Almagro, Fidel, Jenkinson, Mark, Bangerter, Neal K., Andersson, Jesper L.R., Griffanti, Ludovica, Douaud, Gwenaëlle, Sotiropoulos, Stamatios N., Jbabdi, Saad, Hernandez-Fernandez, Moises, Vallee, Emmanuel, Vidaurre, Diego, Webster, Matthew, McCarthy, Paul, Rorden, Christopher, Daducci, Alessandro, Alexander, Daniel C., Zhang, Hui, Dragonu, Iulius, Matthews, Paul M., Miller, Karla L., Smith, Stephen M.

Publikováno v: In NeuroImage 1 February 2018 166:400-424

Unsupervised Domain Adaptation With Optimal Transport in Multi-Site Segmentation of Multiple Sclerosis Lesions From MRI Data

Autor: Ackaouy, Antoine, Courty, Nicolas, Vallée, Emmanuel, Commowick, Olivier, Barillot, Christian, Galassi, Francesca

Publikováno v: Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience, Frontiers, 2020, 14
Frontiers in Computational Neuroscience, Frontiers, 2020, 14, pp.1-13. ⟨10.3389/fncom.2020.00019⟩
Frontiers in Computational Neuroscience, 2020, 14, pp.1-13. ⟨10.3389/fncom.2020.00019⟩
Frontiers in Computational Neuroscience, Vol 14 (2020)

International audience; Automatic segmentation of Multiple Sclerosis (MS) lesions from Magnetic Resonance Imaging (MRI) images is essential for clinical assessment and treatment planning of MS. Recent years have seen an increasing use of Convolutiona

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid_dedup__::dae8703a2d388d0d35ca51c0d2a4c782
https://hal.archives-ouvertes.fr/hal-02317028/document