Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Sarasúa, Ignacio"'
Autor:
Rodríguez-de-Vera, Jesús M, Estepa, Imanol G, Sarasúa, Ignacio, Nagarajan, Bhalaji, Radeva, Petia
In the realm of self-supervised learning (SSL), conventional wisdom has gravitated towards the utility of massive, general domain datasets for pretraining robust backbones. In this paper, we challenge this idea by exploring if it is possible to bridg
Externí odkaz:
http://arxiv.org/abs/2407.03463
Nearest neighbour based methods have proved to be one of the most successful self-supervised learning (SSL) approaches due to their high generalization capabilities. However, their computational efficiency decreases when more than one neighbour is us
Externí odkaz:
http://arxiv.org/abs/2303.09417
Autor:
Villacorta, Pablo, Rodríguez-de-Vera, Jesús M., Bolaños, Marc, Sarasúa, Ignacio, Nagarajan, Bhalaji, Radeva, Petia
Fine-Grained Visual Recognition (FGVR) tackles the problem of distinguishing highly similar categories. One of the main approaches to FGVR, namely subset learning, tries to leverage information from existing class taxonomies to improve the performanc
Externí odkaz:
http://arxiv.org/abs/2303.09269
Autor:
Rickmann, Anne-Marie, Bongratz, Fabian, Pölsterl, Sebastian, Sarasua, Ignacio, Wachinger, Christian
The reconstruction of cerebral cortex surfaces from brain MRI scans is instrumental for the analysis of brain morphology and the detection of cortical thinning in neurodegenerative diseases like Alzheimer's disease (AD). Moreover, for a fine-grained
Externí odkaz:
http://arxiv.org/abs/2210.01772
Autor:
Narazani, Marla, Sarasua, Ignacio, Pölsterl, Sebastian, Lizarraga, Aldana, Yakushev, Igor, Wachinger, Christian
Alzheimer's Disease (AD) is the most common form of dementia and often difficult to diagnose due to the multifactorial etiology of dementia. Recent works on neuroimaging-based computer-aided diagnosis with deep neural networks (DNNs) showed that fusi
Externí odkaz:
http://arxiv.org/abs/2207.02094
Modeling temporal changes in subcortical structures is crucial for a better understanding of the progression of Alzheimer's disease (AD). Given their flexibility to adapt to heterogeneous sequence lengths, mesh-based transformer architectures have be
Externí odkaz:
http://arxiv.org/abs/2207.02091
The longitudinal modeling of neuroanatomical changes related to Alzheimer's disease (AD) is crucial for studying the progression of the disease. To this end, we introduce TransforMesh, a spatio-temporal network based on transformers that models longi
Externí odkaz:
http://arxiv.org/abs/2109.00532
Autor:
Antonelli, Michela, Reinke, Annika, Bakas, Spyridon, Farahani, Keyvan, AnnetteKopp-Schneider, Landman, Bennett A., Litjens, Geert, Menze, Bjoern, Ronneberger, Olaf, Summers, Ronald M., van Ginneken, Bram, Bilello, Michel, Bilic, Patrick, Christ, Patrick F., Do, Richard K. G., Gollub, Marc J., Heckers, Stephan H., Huisman, Henkjan, Jarnagin, William R., McHugo, Maureen K., Napel, Sandy, Pernicka, Jennifer S. Goli, Rhode, Kawal, Tobon-Gomez, Catalina, Vorontsov, Eugene, Meakin, James A., Ourselin, Sebastien, Wiesenfarth, Manuel, Arbelaez, Pablo, Bae, Byeonguk, Chen, Sihong, Daza, Laura, Feng, Jianjiang, He, Baochun, Isensee, Fabian, Ji, Yuanfeng, Jia, Fucang, Kim, Namkug, Kim, Ildoo, Merhof, Dorit, Pai, Akshay, Park, Beomhee, Perslev, Mathias, Rezaiifar, Ramin, Rippel, Oliver, Sarasua, Ignacio, Shen, Wei, Son, Jaemin, Wachinger, Christian, Wang, Liansheng, Wang, Yan, Xia, Yingda, Xu, Daguang, Xu, Zhanwei, Zheng, Yefeng, Simpson, Amber L., Maier-Hein, Lena, Cardoso, M. Jorge
International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation
Externí odkaz:
http://arxiv.org/abs/2106.05735
Geometric deep learning can find representations that are optimal for a given task and therefore improve the performance over pre-defined representations. While current work has mainly focused on point representations, meshes also contain connectivit
Externí odkaz:
http://arxiv.org/abs/2104.10047
Spatial and channel re-calibration have become powerful concepts in computer vision. Their ability to capture long-range dependencies is especially useful for those networks that extract local features, such as CNNs. While re-calibration has been wid
Externí odkaz:
http://arxiv.org/abs/2011.12888