| 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 | 2032 | 2018 |
| Selecting training sets for support vector machines: a review J Nalepa, M Kawulok Artificial Intelligence Review 52 (2), 857-900, 2019 | 387 | 2019 |
| Data augmentation for brain-tumor segmentation: a review J Nalepa, M Marcinkiewicz, M Kawulok Frontiers in computational neuroscience 13, 83, 2019 | 330 | 2019 |
| 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 | 317 | 2017 |
| Spatial-based skin detection using discriminative skin-presence features M Kawulok, J Kawulok, J Nalepa Pattern Recognition Letters 41, 3-13, 2014 | 138 | 2014 |
| Training-and test-time data augmentation for hyperspectral image segmentation J Nalepa, M Myller, M Kawulok IEEE Geoscience and Remote Sensing Letters 17 (2), 292-296, 2019 | 128* | 2019 |
| Adaptive memetic algorithm for minimizing distance in the vehicle routing problem with time windows J Nalepa, M Blocho Soft Computing 20, 2309-2327, 2016 | 111 | 2016 |
| Deep learning for multiple-image super-resolution M Kawulok, P Benecki, S Piechaczek, K Hrynczenko, D Kostrzewa, ... IEEE Geoscience and Remote Sensing Letters 17 (6), 1062-1066, 2019 | 108 | 2019 |
| Self-adaptive algorithm for segmenting skin regions M Kawulok, J Kawulok, J Nalepa, B Smolka EURASIP Journal on Advances in Signal Processing 2014, 1-22, 2014 | 101 | 2014 |
| Hyperspectral band selection using attention-based convolutional neural networks PR Lorenzo, L Tulczyjew, M Marcinkiewicz, J Nalepa IEEE Access 8, 42384-42403, 2020 | 95* | 2020 |
| Validating hyperspectral image segmentation J Nalepa, M Myller, M Kawulok IEEE Geoscience and Remote Sensing Letters 16 (8), 1264-1268, 2019 | 93 | 2019 |
| 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 |
| Unsupervised segmentation of hyperspectral images using 3-D convolutional autoencoders J Nalepa, M Myller, Y Imai, K Honda, T Takeda, M Antoniak IEEE Geoscience and Remote Sensing Letters 17 (11), 1948-1952, 2020 | 76 | 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 | 68 | 2019 |
| Memetic evolution of deep neural networks PR Lorenzo, J Nalepa Proceedings of the genetic and evolutionary computation conference, 505-512, 2018 | 65 | 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 | 61 | 2020 |
| Support vector machines training data selection using a genetic algorithm M Kawulok, J Nalepa Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR …, 2012 | 59 | 2012 |
| Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: a machine learning approach K Drożdż, K Nabrdalik, H Kwiendacz, M Hendel, A Olejarz, A Tomasik, ... Cardiovascular diabetology 21 (1), 240, 2022 | 58 | 2022 |
| 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 |
| Adaptive memetic algorithm enhanced with data geometry analysis to select training data for SVMs J Nalepa, M Kawulok Neurocomputing 185, 113-132, 2016 | 56 | 2016 |
Michal KawulokSilesian University of TechnologyVerified email at polsl.pl