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Pablo Ribalta Lorenzo
Pablo Ribalta Lorenzo
NVIDIA Corporation
Zweryfikowany adres z ieee.org
Tytuł
Cytowane przez
Cytowane przez
Rok
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge
S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ...
arXiv preprint arXiv:1811.02629, 2018
18032018
Particle swarm optimization for hyper-parameter selection in deep neural networks
PR Lorenzo, J Nalepa, M Kawulok, LS Ramos, JR Pastor
Proceedings of the genetic and evolutionary computation conference, 481-488, 2017
2862017
OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization
G Ahdritz, N Bouatta, C Floristean, S Kadyan, Q Xia, W Gerecke, ...
Nature Methods, 1-11, 2024
1022024
Hyper-parameter selection in deep neural networks using parallel particle swarm optimization
PR Lorenzo, J Nalepa, LS Ramos, JR Pastor
Proceedings of the genetic and evolutionary computation conference companion …, 2017
852017
Hyperspectral band selection using attention-based convolutional neural networks
PR Lorenzo, L Tulczyjew, M Marcinkiewicz, J Nalepa
IEEE Access 8, 42384-42403, 2020
642020
Segmenting brain tumors from FLAIR MRI using fully convolutional neural networks
PR Lorenzo, J Nalepa, B Bobek-Billewicz, P Wawrzyniak, G Mrukwa, ...
Computer methods and programs in biomedicine 176, 135-148, 2019
612019
Memetic evolution of deep neural networks
PR Lorenzo, J Nalepa
Proceedings of the genetic and evolutionary computation conference, 505-512, 2018
582018
Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors
J Nalepa, PR Lorenzo, M Marcinkiewicz, B Bobek-Billewicz, ...
Artificial intelligence in medicine 102, 101769, 2020
572020
A reliability study on CNNs for critical embedded systems
MA Neggaz, I Alouani, PR Lorenzo, S Niar
2018 IEEE 36th International Conference on Computer Design (ICCD), 476-479, 2018
562018
Towards resource-frugal deep convolutional neural networks for hyperspectral image segmentation
J Nalepa, M Antoniak, M Myller, PR Lorenzo, M Marcinkiewicz
Microprocessors and Microsystems 73, 102994, 2020
482020
Data augmentation via image registration
J Nalepa, G Mrukwa, S Piechaczek, PR Lorenzo, M Marcinkiewicz, ...
2019 IEEE International Conference on Image Processing (ICIP), 4250-4254, 2019
322019
Segmenting brain tumors from MRI using cascaded multi-modal U-Nets
M Marcinkiewicz, J Nalepa, PR Lorenzo, W Dudzik, G Mrukwa
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2019
322019
Band selection from hyperspectral images using attention-based convolutional neural networks
PR Lorenzo, L Tulczyjew, M Marcinkiewicz, J Nalepa
arXiv preprint arXiv:1811.02667, 2018
222018
Automatic brain tumor segmentation using a two-stage multi-modal fcnn
M Marcinkiewicz, J Nalepa, PR Lorenzo, W Dudzik, G Mrukwa
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2018
152018
Hyperspectral band selection using attention-based convolutional neural networks
P Ribalta Lorenzo, L Tulczyjew, M Marcinkiewicz, J Nalepa
IEEE Access 8, 42384-42403, 2020
132020
Multi-modal U-Nets with boundary loss and pre-training for brain tumor segmentation
P Ribalta Lorenzo, M Marcinkiewicz, J Nalepa
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2020
112020
Convergence analysis of PSO for hyper-parameter selection in deep neural networks
J Nalepa, PR Lorenzo
Advances on P2P, Parallel, Grid, Cloud and Internet Computing: Proceedings …, 2018
112018
Segmentation of hyperspectral images using quantized convolutional neural networks
PR Lorenzo, M Marcinkiewicz, J Nalepa
2018 21st Euromicro Conference on Digital System Design (DSD), 260-267, 2018
42018
ECONIB: AI for fully-automated segmentation and assessment of glioma from DCE-MRI
J Nalepa, PR Lorenzo, M Marcinkiewicz, B Bobek-Billewicz, ...
European Congress of Radiology-ECR 2019, 2019
2019
Band Selection from Hyperspectral Images Using Attention-based Convolutional Neural Networks
P Ribalta Lorenzo, L Tulczyjew, M Marcinkiewicz, J Nalepa
arXiv e-prints, arXiv: 1811.02667, 2018
2018
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