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2019
Combalia M, Codella NCF, Rotemberg V, Helba B, Vilaplana V, Reiter O, et al.. BCN20000: Dermoscopic Lesions in the Wild. In International Skin Imaging Collaboration (ISIC) Challenge on Dermoscopic Skin Lesion Analysis 2019. 2019.
Wang L, Nie D, Li G, Puybareau E, Dolz J, Zhang Q, et al.. Benchmark on Automatic 6-month-old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge. IEEE Transactions on Medical Imaging. 2019;.
Domenech T. Clasificación de imágenes dermatoscópicas utilizando Redes Neuronales Convolucionales e información de metadatos. Vilaplana V. 2019.
Tarrés L. Clasificación de lesiones de piel con un ensemble de redes neuronales residuales. Vilaplana V, Combalia M. 2019.
Isart A, Espasa M, Vilaplana V, Sayrol E. CNN-based bacilli detection in sputum samples for tuberculosis diagnosis. In International Symposium on Biomedical Imaging (ISBI 2019). 2019.
Salgueiro L, Marcello J, Vilaplana V. Comparative study of upsampling methods for super-resolution in remote sensing. In International Conference on Machine Vision. 2019.
Balibrea M. Deep learning for semantic segmentation of airplane hyperspectral imaging. Salgueiro L, Vilaplana V. 2019.
Casamitjana A, Petrone P, Falcon C, Artigues M, Operto G, Cacciaglia R, et al.. Detection of Amyloid Positive Cognitively unimpaired individuals using voxel-based machine learning on structural longitudinal brain MRI. In Alzheimer's Association International Conference. 2019.
Casamitjana A, Petrone P, Falcon C, Artigues M, Operto G, Cacciaglia R, et al.. Detection of Amyloid-Positive Cognitively Unimpaired Individuals Using Voxel-Based Machine Learning on Structural Longitudinal Brain MRI. Alzheimer's & Dementia. 2019;15(754).
Combalia M, Pérez-Anker J, García-Herrera A, Alos L, Vilaplana V, Marques F, et al.. Digitally Stained Confocal Microscopy through Deep Learning. In International Conference on Medical Imaging with Deep Learning (MIDL 2019). London; 2019.
Gené-Mola J, Gregorio E, Guevara J, Cheein FAuat, Sanz R, Escolà A, et al.. Fruit Detection in an Apple Orchard Using a Mobile Terrestrial Laser Scanner. Biosystems Engineering. 2019;187.
Domingo P. Interpretability of Deep Learning Models. Vilaplana V. 2019.
Gené-Mola J, Vilaplana V, Rosell-Polo JR, Morros JR, Ruiz-Hidalgo J, Gregorio E. 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)
Sala M, Soriguera F, Huillca K, Vilaplana V. Measuring traffic lane-changing by converting video into space-time still images. Computer-Aided Civil and Infrastructure Engineering. 2019;.
Gené-Mola J, Vilaplana V, Rosell-Polo JR, Morros JR, Ruiz-Hidalgo J, Gregorio E. Multi-modal Deep Learning for Fuji Apple Detection Using RGB-D Cameras and their Radiometric Capabilities. Computers and Electronics in Agriculture. 2019;162.
Ventura C, Varas D, Vilaplana V, Giró-i-Nieto X, Marques F. Multiresolution co-clustering for uncalibrated multiview segmentation. Signal Processing: Image Communication. 2019;.  (4.35 MB)
Ventura C, Varas D, Vilaplana V, Giró-i-Nieto X, Marques F. Multiresolution co-clustering for uncalibrated multiview segmentation. Signal Processing: Image Communication. 2019;.  (4.35 MB)
Ventura C, Varas D, Vilaplana V, Giró-i-Nieto X, Marques F. Multiresolution co-clustering for uncalibrated multiview segmentation. Signal Processing: Image Communication. 2019;.  (4.35 MB)

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