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 Author  Title  Type  [ Year ]
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. NeAT: a nonlinear analysis toolbox for neuroimaging. Neuroinformatics. 2020;. 
. Projection to Latent Spaces disentangles pathological effects on brain morphology in the asymptomatic phase of Alzheimer’s disease. Frontiers in Neurology, section Applied Neuroimaging. 2020;11. 
. Super-Resolution of Sentinel-2 Imagery Using Generative Adversarial Networks. Remote Sensing. 2020;12(15). 
. Uncertainty Estimation in Deep Neural Networks for Dermoscopic Image Classification. In CVPR 2020, ISIC Skin Image Analysis Workshop. 2020. 
. Weakly Supervised Semantic Segmentation for Remote Sensing Hyperspectral Imaging. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020). 2020. 
. BCN20000: Dermoscopic Lesions in the Wild. In International Skin Imaging Collaboration (ISIC) Challenge on Dermoscopic Skin Lesion Analysis 2019. 2019. 
. Benchmark on Automatic 6-month-old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge. IEEE Transactions on Medical Imaging. 2019;. 
. CNN-based bacilli detection in sputum samples for tuberculosis diagnosis. In International Symposium on Biomedical Imaging (ISBI 2019). 2019. 
. Comparative study of upsampling methods for super-resolution in remote sensing. In International Conference on Machine Vision. 2019. 
. 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). 
. 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. 
. Interpretability of Deep Learning Models. . 2019. 
. 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)
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 (2.43 MB). Measuring traffic lane-changing by converting video into space-time still images. Computer-Aided Civil and Infrastructure Engineering. 2019;. 
. 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)
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 (4.35 MB). Multiresolution co-clustering for uncalibrated multiview segmentation. Signal Processing: Image Communication. 2019;.  (4.35 MB)
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 (4.35 MB). Multiresolution co-clustering for uncalibrated multiview segmentation. Signal Processing: Image Communication. 2019;.  (4.35 MB)
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 (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)
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 (911.15 KB). Plug-and-Train Loss for Model-Based Single View 3D Reconstruction. BMVA Technical Meeting: 3D vision with Deep Learning. London, UK: UPC; 2019.  (3.97 MB)
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