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] . 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)
(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)
(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). 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).
. Retinal lesions segmentation using CNNs and adversarial training. In International Symposium on Biomedical Imaging (ISBI 2019). 2019.
. 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;.
. Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities; Results of the WMH Segmentation Challenge. IEEE Transactions on Medical Imaging. 2019;.
. Study of early stages of Alzheimer’s disease using magnetic resonance imaging. . Signal Theory and Communications. [Barcelona]: Universitat Politècnica de Catalunya; 2019.
. 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). Action Tube Extraction based 3D -CNN for RGB-D Action Recognition. In International Conference on Content-Based Multimedia Indexing CBMI 2018. 2018. (3.09 MB)
(3.09 MB). Brain lesion segmentation using Convolutional Neuronal Networks. . 2018. (3.15 MB)
(3.15 MB). Brain MRI Super-Resolution using Generative Adversarial Networks. In International Conference on Medical Imaging with Deep Learning. Amsterdam, The Netherlands; 2018.
. Cascaded V-Net Using ROI Masks for Brain Tumor Segmentation. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017. Crimi A., Bakas S., Kuijf H., Menze B., Reyes M. (eds). Cham: Springer; 2018. pp. 381-391.
. Characteristic Brain Volumetric Changes in the AD Preclinical Signature. In Alzheimer's Association International Conference. Chicago, USA; 2018.
. Characteristic Brain Volumetric Changes in the AD Preclinical Signature. Alzheimer's & Dementia: The Journal of the Alzheimer's Association. 2018;14(7):P1235.
. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. In MICCAI - Multimodal Brain Tumor Segmentation Challenge. 2018.
. Leishmaniasis Parasite Segmentation and Classification Using Deep Learning. In International Conference on Articulated Motion and Deformable Objects. Palma, Spain; 2018.