| 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 | 2067 | 2018 |
| 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 | 326 | 2017 |
| 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 | 191 | 2024 |
| 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 | 89 | 2017 |
| Hyperspectral band selection using attention-based convolutional neural networks PR Lorenzo, L Tulczyjew, M Marcinkiewicz, J Nalepa IEEE Access 8, 42384-42403, 2020 | 74 | 2020 |
| 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 | 71 | 2019 |
| Memetic evolution of deep neural networks PR Lorenzo, J Nalepa Proceedings of the genetic and evolutionary computation conference, 505-512, 2018 | 66 | 2018 |
| 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 | 62 | 2020 |
| 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 | 61 | 2018 |
| 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 | 56 | 2020 |
| 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 | 36 | 2019 |
| 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 | 36 | 2019 |
| 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 | 23 | 2018 |
| Hyperspectral band selection using attention-based convolutional neural networks P Ribalta Lorenzo, L Tulczyjew, M Marcinkiewicz, J Nalepa IEEE Access 8, 42384-42403, 2020 | 15 | 2020 |
| 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 | 15 | 2018 |
| 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 | 11 | 2020 |
| 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 | 11 | 2018 |
| 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 | 4 | 2018 |
| 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 |
