| Brain tumor segmentation using convolutional neural networks in MRI images S Pereira, A Pinto, V Alves, CA Silva IEEE transactions on medical imaging 35 (5), 1240-1251, 2016 | 2522 | 2016 |
| Deep convolutional neural networks for the segmentation of gliomas in multi-sequence MRI S Pereira, A Pinto, V Alves, CA Silva Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2016 | 156 | 2016 |
| ISLES 2016 and 2017-Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI S Winzeck, A Hakim, R McKinley, JA Pinto, V Alves, C Silva, M Pisov, ... Frontiers in Neurology 9, 2018 | 150 | 2018 |
| Brain tumour segmentation based on extremely randomized forest with high-level features A Pinto, S Pereira, H Correia, J Oliveira, DMLD Rasteiro, CA Silva 2015 37th annual international conference of the IEEE engineering in …, 2015 | 113 | 2015 |
| Hierarchical brain tumour segmentation using extremely randomized trees A Pinto, S Pereira, D Rasteiro, CA Silva Pattern Recognition 82, 105-117, 2018 | 93 | 2018 |
| Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional Networks S Pereira, A Pinto, J Amorim, A Ribeiro, V Alves, CA Silva IEEE transactions on medical imaging 38 (12), 2914-2925, 2019 | 74 | 2019 |
| Stroke Lesion Outcome Prediction based on MRI Imaging combined with Clinical Information A Pinto, R McKinley, V Alves, R Wiest, CA Silva, M Reyes Frontiers in Neurology 9, 1060, 2018 | 68 | 2018 |
| Automatic brain tissue segmentation in MR images using Random Forests and Conditional Random Fields S Pereira, A Pinto, J Oliveira, AM Mendrik, JH Correia, CA Silva Journal of Neuroscience Methods 270, 111-123, 2016 | 61 | 2016 |
| Random decision forests for automatic brain tumor segmentation on multi-modal MRI images A Pinto, S Pereira, H Dinis, CA Silva, DMLD Rasteiro 2015 IEEE 4th Portuguese Meeting on Bioengineering (ENBENG), 1-5, 2015 | 37 | 2015 |
| Enhancing clinical MRI perfusion maps with data-driven maps of complementary nature for lesion outcome prediction A Pinto, S Pereira, R Meier, V Alves, R Wiest, CA Silva, M Reyes Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018 | 36 | 2018 |
| Combining unsupervised and supervised learning for predicting the final stroke lesion A Pinto, S Pereira, R Meier, R Wiest, V Alves, M Reyes, CA Silva Medical image analysis 69, 101888, 2021 | 15 | 2021 |
| Multi-stage deep layer aggregation for brain tumor segmentation CA Silva, A Pinto, S Pereira, A Lopes Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2021 | 15 | 2021 |
| Prediction of Stroke Lesion at 90-Day Follow-Up by Fusing Raw DSC-MRI With Parametric Maps Using Deep Learning A Pinto, J Amorim, A Hakim, V Alves, M Reyes, CA Silva IEEE Access 9, 26260-26270, 2021 | 8 | 2021 |
| Segmentation Squeeze-and-Excitation Blocks in Stroke Lesion Outcome Prediction J Amorim, A Pinto, S Pereira, CA Silva 2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG), 1-4, 2019 | 3 | 2019 |
| Clinical validation of a fully automated framework for ventricular segmentation and wall motion classification from cine CMR E Costa, C Barros, A Pinto, N Martins, L Tondi, S Pica, VH Pereira, ... | | 2022 |
| Segmentação do tecido tumoral cerebral em imagens de ressonância magnética A Pinto | | 2015 |
Carlos A. SilvaUniversidade do MinhoEmail confirmado em dei.uminho.pt
