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. Detection of Amyloid Positive Cognitively unimpaired individuals using voxel-based machine learning on structural longitudinal brain MRI. In Alzheimer's Association International Conference. 2019.
. Detection of Amyloid-Positive Cognitively Unimpaired Individuals Using Voxel-Based Machine Learning on Structural Longitudinal Brain MRI. Alzheimer's & Dementia. 2019;15(754).
. Differential expression of long non-coding RNAs related to proliferation and histological diversity in follicular lymphomas. British Journal of Haematology. 2019;184(3):373-383. 
(980.9 KB)
(980.9 KB). Differential expression of long non-coding RNAs related to proliferation and histological diversity in follicular lymphomas. British Journal of Haematology. 2019;184(3):373-383. (980.9 KB)
(980.9 KB). Digitally Stained Confocal Microscopy through Deep Learning. In International Conference on Medical Imaging with Deep Learning (MIDL 2019). London; 2019.
. Digitally Stained Confocal Microscopy through Deep Learning. In International Conference on Medical Imaging with Deep Learning (MIDL 2019). London; 2019.
. Fruit Detection in an Apple Orchard Using a Mobile Terrestrial Laser Scanner. Biosystems Engineering. 2019;187.
. Integrating low-level motion cues in deep video saliency. . 2019. (10.04 MB)
(10.04 MB). KFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data. Data in Brief. 2019;. (2.43 MB)
(2.43 MB). Multi-modal Deep Learning for Fuji Apple Detection Using RGB-D Cameras and their Radiometric Capabilities. Computers and Electronics in Agriculture. 2019;162.
. Multiresolution co-clustering for uncalibrated multiview segmentation. Signal Processing: Image Communication. 2019;. (4.35 MB)
(4.35 MB). Picking groups instead of samples: A close look at Static Pool-based Meta-Active Learning. In ICCV Workshop - MDALC 2019. Seoul, South Korea; 2019. (911.15 KB)
(911.15 KB). Picking groups instead of samples: A close look at Static Pool-based Meta-Active Learning. In ICCV Workshop - MDALC 2019. Seoul, South Korea; 2019. (911.15 KB)
(911.15 KB). Prediction of a second clinical event in CIS patients by combining lesion and brain features. In Congress of the European Comitee for Treatment and Research in Multiple Sclerosis (ECTRIMS 2019). 2019.
. Prediction of amyloid pathology in cognitively unimpaired individuals using voxelwise analysis of longitudinal structural brain MRI. Alzheimer's Research & Therapy. 2019;11(1).
. Residual Attention Graph Convolutional Network for Geometric 3D Scene Classification. In IEEE Conference on Computer Vision Workshop (ICCVW). Seoul, Korea: IEEE; 2019. (314.43 KB)
(314.43 KB). RVOS: End-to-End Recurrent Network for Video Object Segmentation. In CVPR. Long Beach, CA, USA: OpenCVF / IEEE; 2019. (5.76 MB)
(5.76 MB). Shared latent structures between imaging features and biomarkers in early stages of Alzheimer's disease: a predictive study. IEEE Journal of Biomedical and Health Informatics. 2019;.
. Simple vs complex temporal recurrences for video saliency prediction. In British Machine Vision Conference (BMVC). Cardiff, Wales / UK.: British Machine Vision Association; 2019. (1.79 MB)
(1.79 MB). Simple vs complex temporal recurrences for video saliency prediction. In British Machine Vision Conference (BMVC). Cardiff, Wales / UK.: British Machine Vision Association; 2019. (1.79 MB)
(1.79 MB). Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities; Results of the WMH Segmentation Challenge. IEEE Transactions on Medical Imaging. 2019;.
. Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities; Results of the WMH Segmentation Challenge. IEEE Transactions on Medical Imaging. 2019;.
. Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities; Results of the WMH Segmentation Challenge. IEEE Transactions on Medical Imaging. 2019;.
. Unsupervised GRN Ensemble. In Sanguinetti G., Huynh-Thu V. (eds) Methods in Molecular Biology . New York, NY: Springer science, Humana Press; 2019. pp. 283-302.
. Uso de redes neuronales convolucionales para la detección remota de frutos con cámaras RGB-D. In Congreso Ibérico de Agroingeniería. Huesca: Universidad de Zaragoza (UZA); 2019. (1.21 MB)
(1.21 MB)