Zobrazeno 1 - 10
of 180
pro vyhledávání: '"Canu, Stéphane"'
Autor:
Ruffino, Cyprien, Blin, Rachel, Ainouz, Samia, Gasso, Gilles, Hérault, Romain, Meriaudeau, Fabrice, Canu, Stéphane
Polarimetric imaging, along with deep learning, has shown improved performances on different tasks including scene analysis. However, its robustness may be questioned because of the small size of the training datasets. Though the issue could be solve
Externí odkaz:
http://arxiv.org/abs/2206.07431
The use of pseudo-labels prevails in order to tackle Unsupervised Domain Adaptive (UDA) Re-Identification (re-ID) with the best performance. Indeed, this family of approaches has given rise to several UDA re-ID specific frameworks, which are effectiv
Externí odkaz:
http://arxiv.org/abs/2112.12887
Publikováno v:
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023
Contrastive representation learning has proven to be an effective self-supervised learning method. Most successful approaches are based on Noise Contrastive Estimation (NCE) and use different views of an instance as positives that should be contraste
Externí odkaz:
http://arxiv.org/abs/2111.14585
Publikováno v:
Neurocomputing 2021
Using multimodal Magnetic Resonance Imaging (MRI) is necessary for accurate brain tumor segmentation. The main problem is that not all types of MRIs are always available in clinical exams. Based on the fact that there is a strong correlation between
Externí odkaz:
http://arxiv.org/abs/2111.04735
Publikováno v:
Pattern Recognition 2021
In the field of multimodal segmentation, the correlation between different modalities can be considered for improving the segmentation results. Considering the correlation between different MR modalities, in this paper, we propose a multi-modality se
Externí odkaz:
http://arxiv.org/abs/2111.01623
Unsupervised Domain Adaptation (UDA) for re-identification (re-ID) is a challenging task: to avoid a costly annotation of additional data, it aims at transferring knowledge from a domain with annotated data to a domain of interest with only unlabeled
Externí odkaz:
http://arxiv.org/abs/2110.07897
Brain tumor is one of the most high-risk cancers which causes the 5-year survival rate of only about 36%. Accurate diagnosis of brain tumor is critical for the treatment planning. However, complete data are not always available in clinical scenarios.
Externí odkaz:
http://arxiv.org/abs/2105.13013
Publikováno v:
IEEE Transactions on Image Processing On page(s): 4263-4274 Print ISSN: 1057-7149 Online ISSN: 1941-0042
Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess brain tumor. Accurately segmenting brain tumor from MR images is the key to clinical diagnostics and treatment planning. In addition, multi-modal MR images can provide comp
Externí odkaz:
http://arxiv.org/abs/2104.06231
We established a Spatio-Temporal Neural Network, namely STNN, to forecast the spread of the coronavirus COVID-19 outbreak worldwide in 2020. The basic structure of STNN is similar to the Recurrent Neural Network (RNN) incorporating with not only temp
Externí odkaz:
http://arxiv.org/abs/2103.11860
In the field of multimodal segmentation, the correlation between different modalities can be considered for improving the segmentation results. In this paper, we propose a multi-modality segmentation network with a correlation constraint. Our network
Externí odkaz:
http://arxiv.org/abs/2102.03111